├── .gitignore ├── LICENSE ├── README.md ├── SummaC - Additional Experiments.ipynb ├── SummaC - Main Results.ipynb ├── requirements.txt ├── script.sh ├── setup.py ├── summac ├── __init__.py ├── benchmark.py ├── model_baseline.py ├── model_guardrails.py ├── model_summac.py ├── run_baseline.py ├── train_summac.py ├── utils_misc.py ├── utils_optim.py ├── utils_scorer.py └── utils_scoring.py └── summac_conv_vitc_sent_perc_e.bin /.gitignore: -------------------------------------------------------------------------------- 1 | env/ 2 | __pycache__/ 3 | .vscode/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SummaC: Summary Consistency Detection 2 | 3 | This repository contains the code for TACL2021 paper: SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization 4 | 5 | We release: (1) the trained SummaC models, (2) the SummaC Benchmark and data loaders, (3) training and evaluation scripts. 6 | 7 |

8 | 9 |

10 | 11 | ## Installing/Using SummaC 12 | 13 | [Update] Thanks to @Aktsvigun for the help, we now have a pip package, making it easy to install the SummaC models: 14 | ``` 15 | pip install summac 16 | ``` 17 | 18 | Requirement issues: in v0.0.4, we've reduced package dependencies to facilitate installation. We recommend you install `torch` first and verify it works before installing `summac`. 19 | 20 | The two trained models SummaC-ZS and SummaC-Conv are implemented in `model_summac` ([link](https://github.com/tingofurro/summac/blob/master/model_summac.py)). Once the package is installed, the models can be used like this: 21 | 22 | ### Example use 23 | 24 | ```python 25 | from summac.model_summac import SummaCZS, SummaCConv 26 | 27 | model_zs = SummaCZS(granularity="sentence", model_name="vitc", device="cpu") # If you have a GPU: switch to: device="cuda" 28 | model_conv = SummaCConv(models=["vitc"], bins='percentile', granularity="sentence", nli_labels="e", device="cpu", start_file="default", agg="mean") 29 | 30 | document = """Scientists are studying Mars to learn about the Red Planet and find landing sites for future missions. 31 | One possible site, known as Arcadia Planitia, is covered instrange sinuous features. 32 | The shapes could be signs that the area is actually made of glaciers, which are large masses of slow-moving ice. 33 | Arcadia Planitia is in Mars' northern lowlands.""" 34 | 35 | summary1 = "There are strange shape patterns on Arcadia Planitia. The shapes could indicate the area might be made of glaciers. This makes Arcadia Planitia ideal for future missions." 36 | score_zs1 = model_zs.score([document], [summary1]) 37 | score_conv1 = model_conv.score([document], [summary1]) 38 | print("[Summary 1] SummaCZS Score: %.3f; SummacConv score: %.3f" % (score_zs1["scores"][0], score_conv1["scores"][0])) # [Summary 1] SummaCZS Score: 0.582; SummacConv score: 0.536 39 | 40 | summary2 = "There are strange shape patterns on Arcadia Planitia. The shapes could indicate the area might be made of glaciers." 41 | score_zs2 = model_zs.score([document], [summary2]) 42 | score_conv2 = model_conv.score([document], [summary2]) 43 | print("[Summary 2] SummaCZS Score: %.3f; SummacConv score: %.3f" % (score_zs2["scores"][0], score_conv2["scores"][0])) # [Summary 2] SummaCZS Score: 0.877; SummacConv score: 0.709 44 | ``` 45 | 46 | We recommend using the SummaCConv models, as experiments from the paper show it provides better predictions. Two notebooks provide experimental details: [SummaC - Main Results.ipynb](https://github.com/tingofurro/summac/blob/master/SummaC%20-%20Main%20Results.ipynb) for the main results (Table 2) and [SummaC - Additional Experiments.ipynb](https://github.com/tingofurro/summac/blob/master/SummaC%20-%20Additional%20Experiments.ipynb) for additional experiments (Tables 1, 3, 4, 5, 6) from the paper. 47 | 48 | ### SummaC Benchmark 49 | 50 | The SummaC Benchmark consists of 6 summary consistency datasets that have been standardized to a binary classification task. The datasets included are: 51 | 52 |

53 |
54 | % Positive is the percentage of positive (consistent) summaries. IAA is the inter-annotator agreement (Fleiss Kappa). Source is the dataset used for the source documents (CNN/DM or XSum). # Summarizers is the number of summarizers (extractive and abstractive) included in the dataset. # Sublabel is the number of labels in the typology used to label summary errors. 55 |

56 | 57 | The data-loaders for the benchmark are included in `benchmark.py` ([link](https://github.com/tingofurro/summac/blob/master/summac/benchmark.py)). Each dataset in the benchmark downloads automatically on first run. To load the benchmark: 58 | ```py 59 | from summac.benchmark import SummaCBenchmark 60 | benchmark_val = SummaCBenchmark(benchmark_folder="/path/to/summac_benchmark/", cut="val", hf_datasets_cache_dir = "/path/to/huggingface_datasets_cache_dir/") 61 | frank_dataset = benchmark_val.get_dataset("frank") 62 | print(frank_dataset[300]) # {"document: "A Darwin woman has become a TV [...]", "claim": "natalia moon , 23 , has become a tv sensation [...]", "label": 0, "cut": "val", "model_name": "s2s", "error_type": "LinkE"} 63 | ``` 64 | 65 | 66 | 67 | ## Cite the work 68 | 69 | If you make use of the code, models, or algorithm, please cite our paper. 70 | ``` 71 | @article{Laban2022SummaCRN, 72 | title={SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization}, 73 | author={Philippe Laban and Tobias Schnabel and Paul N. Bennett and Marti A. Hearst}, 74 | journal={Transactions of the Association for Computational Linguistics}, 75 | year={2022}, 76 | volume={10}, 77 | pages={163-177} 78 | } 79 | ``` 80 | 81 | ## Contributing 82 | 83 | If you'd like to contribute, or have questions or suggestions, you can contact us at phillab@berkeley.edu. All contributions welcome, for example helping make the benchmark more easily downloadable, or improving model performance on the benchmark. 84 | -------------------------------------------------------------------------------- /SummaC - Additional Experiments.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "2021-10-31 22:47:43,882 [3845] WARNING datasets.builder:355: [JupyterRequire] Using custom data configuration default\n", 13 | "2021-10-31 22:47:43,888 [3845] WARNING datasets.builder:510: [JupyterRequire] Reusing dataset xsum (/home/phillab/.cache/huggingface/datasets/xsum/default/1.2.0/4957825a982999fbf80bca0b342793b01b2611e021ef589fb7c6250b3577b499)\n", 14 | "2021-10-31 22:47:48,828 [3845] WARNING datasets.builder:510: [JupyterRequire] Reusing dataset cnn_dailymail (/home/phillab/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234)\n" 15 | ] 16 | }, 17 | { 18 | "name": "stdout", 19 | "output_type": "stream", 20 | "text": [ 21 | " name N N_pos N_neg frac_pos\n", 22 | "0 cogensumm 400 312 88 0.780000\n", 23 | "1 xsumfaith 1250 130 1120 0.104000\n", 24 | "2 polytope 634 41 593 0.064669\n", 25 | "3 factcc 503 441 62 0.876740\n", 26 | "4 summeval 850 770 80 0.905882\n", 27 | "5 frank 1575 529 1046 0.335873\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "import sklearn, torch, numpy as np, json, os, tqdm, pandas as pd, nltk, utils_misc, seaborn as sns, sys, glob\n", 33 | "sys.path.insert(0, \"/home/phillab/summac/\")\n", 34 | "from model_summac import SummaCConv, SummaCZS, model_map\n", 35 | "from utils_summac_benchmark import SummaCBenchmark\n", 36 | "from utils_scoring import ScorerWrapper\n", 37 | "import utils_summac_benchmark\n", 38 | "\n", 39 | "cm = sns.light_palette(\"green\", as_cmap=True)\n", 40 | "benchmark = SummaCBenchmark(cut=\"test\")\n", 41 | "benchmark.print_stats()\n", 42 | "\n", 43 | "def path_to_model_info(file_path):\n", 44 | " toks = file_path.split(\"/\")\n", 45 | " file_name = toks[-1].replace(\".bin\", \"\")\n", 46 | " model_type = \"histo\"\n", 47 | " model_card, granularity, bins, nli_labels, acc = file_name.split(\"_\")\n", 48 | " acc = float(acc.replace(\"bacc\", \"\").replace(\"f1\", \"\"))\n", 49 | " return {\"model_type\": model_type, \"model_card\": model_card, \"granularity\": granularity, \"bins\": bins, \"acc\": acc, \"model_path\": file_path, \"nli_labels\": nli_labels}" 50 | ] 51 | }, 52 | { 53 | "cell_type": "markdown", 54 | "metadata": { 55 | "heading_collapsed": true 56 | }, 57 | "source": [ 58 | "# Table 3: NLI Model Selection\n" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 2, 64 | "metadata": { 65 | "hidden": true, 66 | "scrolled": false 67 | }, 68 | "outputs": [ 69 | { 70 | "name": "stdout", 71 | "output_type": "stream", 72 | "text": [ 73 | "\n", 74 | "\n", 75 | "\n", 76 | "\n", 77 | "\n", 78 | "\n", 79 | "\n", 80 | "\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "scorers = []\n", 86 | "model_keys = list(model_map.keys())#+ [\"decomp\"]\n", 87 | "# model_keys = [\"decomp\"]\n", 88 | "\n", 89 | "for model_key in model_keys:\n", 90 | " scorers.append({\"name\": \"ZS-%s\" % (model_key.upper().replace(\"-\", \"_\")), \"model\": SummaCZS(granularity=\"sentence\", model_name=model_key), \"sign\": 1, \"only_doc\": True})\n", 91 | " \n", 92 | " # Add a histogram based-model\n", 93 | " model_files = glob.glob(\"/home/phillab/models/summac/%s_sentence*\" % (model_key))\n", 94 | " if len(model_files) == 0:\n", 95 | " print(\"No model for [%s] was found\" % (model_key))\n", 96 | " continue\n", 97 | " best = sorted([path_to_model_info(mf) for mf in model_files], key=lambda m: m[\"acc\"])[-1]\n", 98 | " scorers.append({\"name\": \"Histo-%s\" % (model_key.upper().replace(\"-\", \"_\")), \"model\": SummaCConv(bins=best[\"bins\"], nli_labels=best[\"nli_labels\"], models=[model_key], granularity=\"sentence\", start_file=best[\"model_path\"]), \"sign\": 1})\n", 99 | "\n", 100 | "scorer_doc = ScorerWrapper(scorers, scoring_method=\"sum\", max_batch_size=20, use_caching=True)" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 3, 106 | "metadata": { 107 | "hidden": true, 108 | "scrolled": false 109 | }, 110 | "outputs": [ 111 | { 112 | "name": "stderr", 113 | "output_type": "stream", 114 | "text": [ 115 | "Using custom data configuration default\n", 116 | "Reusing dataset xsum (/home/phillab/.cache/huggingface/datasets/xsum/default/1.2.0/4957825a982999fbf80bca0b342793b01b2611e021ef589fb7c6250b3577b499)\n", 117 | "Reusing dataset cnn_dailymail (/home/phillab/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234)\n", 118 | " 0%| | 0/400 [00:00\n", 232 | "\n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | "
Balanced Accuracy
score
nli_name model_type
ANLIHisto0.699
ZS0.717
MNLIHisto0.73
ZS0.709
MNLI_BASEHisto0.698
ZS0.695
SNLI_BASEHisto0.64
ZS0.666
SNLI_LARGEHisto0.624
ZS0.666
VITCHisto0.74
ZS0.721
VITC_BASEHisto0.712
ZS0.679
VITC_ONLYHisto0.728
ZS0.711
" 306 | ], 307 | "text/plain": [ 308 | "" 309 | ] 310 | }, 311 | "execution_count": 3, 312 | "metadata": {}, 313 | "output_type": "execute_result" 314 | } 315 | ], 316 | "source": [ 317 | "benchmark = SummaCBenchmark(cut=\"test\")\n", 318 | "\n", 319 | "results = {}\n", 320 | "for dataset in benchmark.tasks:\n", 321 | " print(\"======= %s ========\" % (dataset[\"name\"]))\n", 322 | " datas = dataset[\"task\"]\n", 323 | " labels = [d[\"label\"] for d in datas]\n", 324 | " utils_summac_benchmark.compute_doc_level(scorer_doc, datas)\n", 325 | " \n", 326 | " for pred_label in datas[0].keys():\n", 327 | " if \"pred_\" not in pred_label or \"total\" in pred_label: continue\n", 328 | " balanced_acc = sklearn.metrics.balanced_accuracy_score(labels, [d[pred_label] for d in datas])\n", 329 | " model_name, input_type = pred_label.replace(\"pred_\", \"\").split(\"|\")\n", 330 | " model_type, nli_name = model_name.split(\"-\")\n", 331 | " k = (model_type, nli_name)\n", 332 | " if k not in results:\n", 333 | " results[k] = []\n", 334 | " results[k].append(balanced_acc)\n", 335 | "\n", 336 | "cleaned_results = []\n", 337 | "for (model_type, nli), vs in results.items():\n", 338 | " cleaned_results.append({\"nli_name\": nli, \"model_type\": model_type, \"score\": np.mean(vs)})\n", 339 | " \n", 340 | "pd.DataFrame(cleaned_results).groupby([\"nli_name\", \"model_type\"]).agg({\"score\": \"sum\"}).style.set_precision(3).set_caption(\"Balanced Accuracy\")" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": 4, 346 | "metadata": { 347 | "hidden": true 348 | }, 349 | "outputs": [], 350 | "source": [ 351 | "for scorer in scorers:\n", 352 | " scorer[\"model\"].save_imager_cache()" 353 | ] 354 | }, 355 | { 356 | "cell_type": "markdown", 357 | "metadata": { 358 | "heading_collapsed": true 359 | }, 360 | "source": [ 361 | "# Table 4: Choice of NLI Category" 362 | ] 363 | }, 364 | { 365 | "cell_type": "code", 366 | "execution_count": null, 367 | "metadata": { 368 | "hidden": true 369 | }, 370 | "outputs": [], 371 | "source": [ 372 | "scorers = []\n", 373 | "for model_key in [\"vitc\", \"mnli\", \"anli\"]:\n", 374 | " for nli_labels in [\"e\", \"c\", \"n\", \"ec\", \"en\", \"cn\", \"ecn\"]:\n", 375 | " \n", 376 | " model_files = glob.glob(\"/home/phillab/models/summac/%s_sentence_percentile_%s*\" % (model_key, nli_labels))\n", 377 | " if len(model_files) == 0:\n", 378 | " print(\"No model for [%s, %s] was found\" % (model_key, nli_labels))\n", 379 | " continue\n", 380 | " best = sorted([path_to_model_info(mf) for mf in model_files], key=lambda m: m[\"acc\"])[-1]\n", 381 | " scorers.append({\"name\": \"Histo-%s-%s\" % (model_key.upper().replace(\"-\", \"_\"), nli_labels), \"model\": SummaCConv(bins=best[\"bins\"], nli_labels=best[\"nli_labels\"], models=[model_key], granularity=\"sentence\", start_file=best[\"model_path\"]), \"sign\": 1})\n", 382 | "\n", 383 | "scorer_doc = ScorerWrapper(scorers, max_batch_size=20, use_caching=True)\n", 384 | "print(\"%d scorers loaded\" % (len(scorers)))\n", 385 | "\n", 386 | "benchmark = SummaCBenchmark(cut=\"test\")\n", 387 | "\n", 388 | "results = {}\n", 389 | "for dataset in benchmark.tasks:\n", 390 | " print(\"======= %s ========\" % (dataset[\"name\"]))\n", 391 | " datas = dataset[\"task\"]\n", 392 | " labels = [d[\"label\"] for d in datas]\n", 393 | " utils_summac_benchmark.compute_doc_level(scorer_doc, datas)\n", 394 | " \n", 395 | " for pred_label in datas[0].keys():\n", 396 | " if \"pred_\" not in pred_label or \"total\" in pred_label: continue\n", 397 | " balanced_acc = sklearn.metrics.balanced_accuracy_score(labels, [d[pred_label] for d in datas])\n", 398 | " model_name, input_type = pred_label.replace(\"pred_\", \"\").split(\"|\")\n", 399 | " model_type, nli_name, nli_labels = model_name.split(\"-\")\n", 400 | " k = (nli_name, nli_labels)\n", 401 | " if k not in results:\n", 402 | " results[k] = []\n", 403 | " results[k].append(balanced_acc)\n", 404 | "\n", 405 | "cleaned_results = []\n", 406 | "for (nli, nli_labels), vs in results.items():\n", 407 | " cleaned_results.append({\"nli_name\": nli, \"nli_labels\": nli_labels, \"model_type\": model_type, \"score\": np.mean(vs)})\n", 408 | " \n", 409 | "pd.DataFrame(cleaned_results).groupby([\"nli_name\", \"nli_labels\"]).agg({\"score\": \"sum\"}).style.set_precision(3).set_caption(\"Balanced Accuracy\")" 410 | ] 411 | }, 412 | { 413 | "cell_type": "markdown", 414 | "metadata": { 415 | "heading_collapsed": true 416 | }, 417 | "source": [ 418 | "# Table 5: Granularity Selection\n" 419 | ] 420 | }, 421 | { 422 | "cell_type": "code", 423 | "execution_count": 2, 424 | "metadata": { 425 | "hidden": true 426 | }, 427 | "outputs": [ 428 | { 429 | "name": "stdout", 430 | "output_type": "stream", 431 | "text": [ 432 | "\n", 433 | "\n", 434 | "\n", 435 | "\n", 436 | "\n", 437 | "\n", 438 | "\n", 439 | "\n", 440 | "\n", 441 | "\n", 442 | "\n", 443 | "\n", 444 | "28 scorers loaded\n" 445 | ] 446 | } 447 | ], 448 | "source": [ 449 | "scorers = []\n", 450 | "granularities = [\"document\", \"document-sentence\", \"paragraph-document\", \"paragraph-sentence\", \"2sents-document\", \"2sents-sentence\", \"sentence-document\", \"sentence\"]\n", 451 | "\n", 452 | "for model_key in [\"mnli\", \"vitc\"]:\n", 453 | "# for granularity in [\"document\", \"document-sentence\", \"paragraph-document\", \"paragraph-sentence\", \"2sents-sentence\", \"sentence-document\", \"sentence\"]:\n", 454 | " for granularity in granularities:\n", 455 | "# for granularity in [\"sentence-document\"]:\n", 456 | " scorers.append({\"name\": \"ZS-%s-%s\" % (model_key.upper().replace(\"-\", \"_\"), granularity.replace(\"-\", \"_\")), \"model\": SummaCZS(granularity=granularity, model_name=model_key), \"sign\": 1})\n", 457 | " if granularity.startswith(\"document\"):\n", 458 | " continue\n", 459 | " model_files = glob.glob(\"/home/phillab/models/summac/%s_%s*\" % (model_key, granularity))\n", 460 | " if len(model_files) == 0:\n", 461 | " print(\"No model for [%s, %s] was found\" % (model_key, granularity))\n", 462 | " continue\n", 463 | " best = sorted([path_to_model_info(mf) for mf in model_files], key=lambda m: m[\"acc\"])[-1]\n", 464 | " scorers.append({\"name\": \"Histo-%s-%s\" % (model_key.upper().replace(\"-\", \"_\"), granularity.replace(\"-\", \"_\")), \"model\": SummaCConv(bins=best[\"bins\"], nli_labels=best[\"nli_labels\"], models=[model_key], granularity=granularity, start_file=best[\"model_path\"]), \"sign\": 1})\n", 465 | "\n", 466 | "scorer_doc = ScorerWrapper(scorers, max_batch_size=20, use_caching=True)\n", 467 | "print(\"%d scorers loaded\" % (len(scorers)))" 468 | ] 469 | }, 470 | { 471 | "cell_type": "code", 472 | "execution_count": 3, 473 | "metadata": { 474 | "hidden": true 475 | }, 476 | "outputs": [ 477 | { 478 | "name": "stderr", 479 | "output_type": "stream", 480 | "text": [ 481 | " 10%|█ | 40/400 [00:00<00:01, 304.49it/s]" 482 | ] 483 | }, 484 | { 485 | "name": "stdout", 486 | "output_type": "stream", 487 | "text": [ 488 | "======= cogensumm ========\n" 489 | ] 490 | }, 491 | { 492 | "name": "stderr", 493 | "output_type": "stream", 494 | "text": [ 495 | "100%|██████████| 400/400 [00:01<00:00, 326.26it/s]\n", 496 | " 3%|▎ | 40/1250 [00:00<00:03, 326.44it/s]" 497 | ] 498 | }, 499 | { 500 | "name": "stdout", 501 | "output_type": "stream", 502 | "text": [ 503 | "======= xsumfaith ========\n" 504 | ] 505 | }, 506 | { 507 | "name": "stderr", 508 | "output_type": "stream", 509 | "text": [ 510 | "100%|██████████| 1250/1250 [00:02<00:00, 462.43it/s]\n", 511 | " 6%|▋ | 40/634 [00:00<00:01, 330.76it/s]" 512 | ] 513 | }, 514 | { 515 | "name": "stdout", 516 | "output_type": "stream", 517 | "text": [ 518 | "======= polytope ========\n" 519 | ] 520 | }, 521 | { 522 | "name": "stderr", 523 | "output_type": "stream", 524 | "text": [ 525 | "100%|██████████| 634/634 [00:01<00:00, 386.73it/s]\n", 526 | " 8%|▊ | 40/503 [00:00<00:01, 341.95it/s]" 527 | ] 528 | }, 529 | { 530 | "name": "stdout", 531 | "output_type": "stream", 532 | "text": [ 533 | "======= factcc ========\n" 534 | ] 535 | }, 536 | { 537 | "name": "stderr", 538 | "output_type": "stream", 539 | "text": [ 540 | "100%|██████████| 503/503 [00:01<00:00, 441.78it/s]\n", 541 | " 5%|▍ | 40/850 [00:00<00:02, 356.76it/s]" 542 | ] 543 | }, 544 | { 545 | "name": "stdout", 546 | "output_type": "stream", 547 | "text": [ 548 | "======= summeval ========\n" 549 | ] 550 | }, 551 | { 552 | "name": "stderr", 553 | "output_type": "stream", 554 | "text": [ 555 | "100%|██████████| 850/850 [00:02<00:00, 350.46it/s]\n", 556 | " 4%|▍ | 60/1575 [00:00<00:03, 423.98it/s]" 557 | ] 558 | }, 559 | { 560 | "name": "stdout", 561 | "output_type": "stream", 562 | "text": [ 563 | "======= frank ========\n" 564 | ] 565 | }, 566 | { 567 | "name": "stderr", 568 | "output_type": "stream", 569 | "text": [ 570 | "100%|██████████| 1575/1575 [00:03<00:00, 394.82it/s]\n" 571 | ] 572 | }, 573 | { 574 | "data": { 575 | "text/html": [ 576 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Balanced Accuracy
score
nli_name granularity model_type
MNLI2sents_documentHisto0.638
ZS0.64
2sents_sentenceHisto0.737
ZS0.712
documentZS0.564
document_sentenceZS0.574
paragraph_documentHisto0.616
ZS0.598
paragraph_sentenceHisto0.647
ZS0.652
sentenceHisto0.73
ZS0.703
sentence_documentHisto0.62
ZS0.587
VITC2sents_documentHisto0.713
ZS0.697
2sents_sentenceHisto0.741
ZS0.725
documentZS0.721
document_sentenceZS0.731
paragraph_documentHisto0.712
ZS0.698
paragraph_sentenceHisto0.743
ZS0.726
sentenceHisto0.74
ZS0.718
sentence_documentHisto0.697
ZS0.684
" 709 | ], 710 | "text/plain": [ 711 | "" 712 | ] 713 | }, 714 | "execution_count": 3, 715 | "metadata": {}, 716 | "output_type": "execute_result" 717 | } 718 | ], 719 | "source": [ 720 | "results = {}\n", 721 | "for dataset in benchmark.tasks:\n", 722 | " print(\"======= %s ========\" % (dataset[\"name\"]))\n", 723 | " datas = dataset[\"task\"]\n", 724 | " labels = [d[\"label\"] for d in datas]\n", 725 | " utils_summac_benchmark.compute_doc_level(scorer_doc, datas)\n", 726 | " \n", 727 | " # Can be removed if re-running\n", 728 | " for scorer in scorers:\n", 729 | " scorer[\"model\"].save_imager_cache()\n", 730 | " \n", 731 | " for pred_label in datas[0].keys():\n", 732 | " if \"pred_\" not in pred_label or \"total\" in pred_label: continue\n", 733 | " balanced_acc = sklearn.metrics.balanced_accuracy_score(labels, [d[pred_label] for d in datas])\n", 734 | " model_name, input_type = pred_label.replace(\"pred_\", \"\").split(\"|\")\n", 735 | " \n", 736 | " model_type, nli_name, gran = model_name.split(\"-\")\n", 737 | " k = (model_type, nli_name, gran)\n", 738 | " if k not in results:\n", 739 | " results[k] = []\n", 740 | " results[k].append(balanced_acc)\n", 741 | "\n", 742 | "cleaned_results = []\n", 743 | "for (model_type, nli, gran), vs in results.items():\n", 744 | " cleaned_results.append({\"nli_name\": nli, \"granularity\": gran, \"model_type\": model_type, \"score\": np.mean(vs)})\n", 745 | " \n", 746 | "pd.DataFrame(cleaned_results).groupby([\"nli_name\", \"granularity\", \"model_type\"]).agg({\"score\": \"sum\"}).style.set_precision(3).set_caption(\"Balanced Accuracy\")" 747 | ] 748 | }, 749 | { 750 | "cell_type": "markdown", 751 | "metadata": {}, 752 | "source": [ 753 | "# Table 6: SummaCZS Operator Choice" 754 | ] 755 | }, 756 | { 757 | "cell_type": "code", 758 | "execution_count": 7, 759 | "metadata": {}, 760 | "outputs": [ 761 | { 762 | "name": "stdout", 763 | "output_type": "stream", 764 | "text": [ 765 | "9 scorers loaded\n" 766 | ] 767 | } 768 | ], 769 | "source": [ 770 | "scorers = []\n", 771 | "for op1 in [\"min\", \"mean\", \"max\"]:\n", 772 | " for op2 in [\"min\", \"mean\", \"max\"]:\n", 773 | " scorers.append({\"name\": \"ZS-%s-%s\" % (op1, op2), \"model\": SummaCZS(granularity=\"sentence\", model_name=\"vitc\", op1=op1, op2=op2), \"sign\": 1})\n", 774 | " \n", 775 | "scorer_doc = ScorerWrapper(scorers, max_batch_size=20, use_caching=True)\n", 776 | "print(\"%d scorers loaded\" % (len(scorers)))" 777 | ] 778 | }, 779 | { 780 | "cell_type": "code", 781 | "execution_count": 8, 782 | "metadata": {}, 783 | "outputs": [ 784 | { 785 | "name": "stderr", 786 | "output_type": "stream", 787 | "text": [ 788 | "2021-07-31 14:15:34,909 [6185] WARNING datasets.builder:355: [JupyterRequire] Using custom data configuration default\n", 789 | "2021-07-31 14:15:34,912 [6185] WARNING datasets.builder:510: [JupyterRequire] Reusing dataset xsum 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1250/1250 [00:00<00:00, 4622.31it/s]\n", 821 | "100%|██████████| 634/634 [00:00<00:00, 4340.38it/s]" 822 | ] 823 | }, 824 | { 825 | "name": "stdout", 826 | "output_type": "stream", 827 | "text": [ 828 | "======= polytope ========\n" 829 | ] 830 | }, 831 | { 832 | "name": "stderr", 833 | "output_type": "stream", 834 | "text": [ 835 | "\n", 836 | "100%|██████████| 503/503 [00:00<00:00, 4521.10it/s]" 837 | ] 838 | }, 839 | { 840 | "name": "stdout", 841 | "output_type": "stream", 842 | "text": [ 843 | "======= factcc ========\n" 844 | ] 845 | }, 846 | { 847 | "name": "stderr", 848 | "output_type": "stream", 849 | "text": [ 850 | "\n", 851 | " 52%|█████▏ | 440/850 [00:00<00:00, 4396.31it/s]" 852 | ] 853 | }, 854 | { 855 | "name": "stdout", 856 | "output_type": "stream", 857 | "text": [ 858 | "======= summeval ========\n" 859 | ] 860 | }, 861 | { 862 | "name": "stderr", 863 | "output_type": "stream", 864 | "text": [ 865 | "100%|██████████| 850/850 [00:00<00:00, 3651.48it/s]\n", 866 | " 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Balanced Accuracy
score
op1 op2
maxmax0.691
mean0.718
min0.72
meanmax0.62
mean0.628
min0.605
minmax0.574
mean0.557
min0.531
" 928 | ], 929 | "text/plain": [ 930 | "" 931 | ] 932 | }, 933 | "execution_count": 8, 934 | "metadata": {}, 935 | "output_type": "execute_result" 936 | } 937 | ], 938 | "source": [ 939 | "benchmark = SummaCBenchmark(cut=\"test\")\n", 940 | "\n", 941 | "results = {}\n", 942 | "for dataset in benchmark.tasks:\n", 943 | " print(\"======= %s ========\" % (dataset[\"name\"]))\n", 944 | " datas = dataset[\"task\"]\n", 945 | " labels = [d[\"label\"] for d in datas]\n", 946 | " utils_summac_benchmark.compute_doc_level(scorer_doc, datas)\n", 947 | " \n", 948 | " for pred_label in datas[0].keys():\n", 949 | " if \"pred_\" not in pred_label or \"total\" in pred_label: continue\n", 950 | " balanced_acc = sklearn.metrics.balanced_accuracy_score(labels, [d[pred_label] for d in datas])\n", 951 | " model_name, input_type = pred_label.replace(\"pred_\", \"\").split(\"|\")\n", 952 | " model_type, op1, op2 = model_name.split(\"-\")\n", 953 | " k = (op1, op2)\n", 954 | " if k not in results:\n", 955 | " results[k] = []\n", 956 | " results[k].append(balanced_acc)\n", 957 | "\n", 958 | "cleaned_results = []\n", 959 | "for (op1, op2), vs in results.items():\n", 960 | " cleaned_results.append({\"op1\": op1, \"op2\": op2, \"score\": np.mean(vs)})\n", 961 | " \n", 962 | "pd.DataFrame(cleaned_results).groupby([\"op1\", \"op2\"]).agg({\"score\": \"sum\"}).style.set_precision(3).set_caption(\"Balanced Accuracy\")" 963 | ] 964 | } 965 | ], 966 | "metadata": { 967 | "finalized": { 968 | "timestamp": 1625710190289, 969 | "trusted": true 970 | }, 971 | "kernelspec": { 972 | "display_name": "Python 3", 973 | "language": "python", 974 | "name": "python3" 975 | }, 976 | "language_info": { 977 | "codemirror_mode": { 978 | "name": "ipython", 979 | "version": 3 980 | }, 981 | "file_extension": ".py", 982 | "mimetype": "text/x-python", 983 | "name": "python", 984 | "nbconvert_exporter": "python", 985 | "pygments_lexer": "ipython3", 986 | "version": "3.7.6" 987 | } 988 | }, 989 | "nbformat": 4, 990 | "nbformat_minor": 4 991 | } 992 | -------------------------------------------------------------------------------- /SummaC - Main Results.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using custom data configuration default\n", 13 | "Reusing dataset xsum (/home/phillab/.cache/huggingface/datasets/xsum/default/1.2.0/4957825a982999fbf80bca0b342793b01b2611e021ef589fb7c6250b3577b499)\n", 14 | "Reusing dataset cnn_dailymail (/home/phillab/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/3cb851bf7cf5826e45d49db2863f627cba583cbc32342df7349dfe6c38060234)\n" 15 | ] 16 | }, 17 | { 18 | "name": "stdout", 19 | "output_type": "stream", 20 | "text": [ 21 | " name N N_pos N_neg frac_pos\n", 22 | "0 cogensumm 400 312 88 0.780000\n", 23 | "1 xsumfaith 1250 130 1120 0.104000\n", 24 | "2 polytope 634 41 593 0.064669\n", 25 | "3 factcc 503 441 62 0.876740\n", 26 | "4 summeval 850 770 80 0.905882\n", 27 | "5 frank 1575 529 1046 0.335873\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "from utils_summac_benchmark import SummaCBenchmark\n", 33 | "import utils_summac_benchmark, random\n", 34 | "\n", 35 | "benchmark = SummaCBenchmark(benchmark_folder=\"/home/phillab/data/summac_benchmark/\", cut=\"test\")\n", 36 | "benchmark.print_stats()" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": {}, 42 | "source": [ 43 | "# Table 2: Main Table of Results\n" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 2, 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "name": "stdout", 53 | "output_type": "stream", 54 | "text": [ 55 | "\n" 56 | ] 57 | } 58 | ], 59 | "source": [ 60 | "import sklearn, torch, numpy as np, json, os, tqdm, pandas as pd, nltk, seaborn as sns\n", 61 | "from model_guardrails import NERInaccuracyPenalty\n", 62 | "from model_summac import SummaCConv, SummaCZS\n", 63 | "from model_baseline import BaselineScorer\n", 64 | "# from model_entailment import EntailmentScorer\n", 65 | "from model_classifier import Classifier\n", 66 | "from utils_scoring import ScorerWrapper\n", 67 | "\n", 68 | "use_cache = True\n", 69 | "scorers = [\n", 70 | " {\"name\": \"NER\", \"model\": NERInaccuracyPenalty(flipped=True), \"only_doc\": True, \"sign\": 1},\n", 71 | "# {\"name\": \"MNLI\", \"model\": EntailmentScorer(model_card=\"roberta-large-mnli\", contradiction_idx=0), \"sign\": 1},\n", 72 | " # {\"name\": \"FactCC-CLS\", \"model\": Classifier(model_card=\"roberta-base\", score_class=1, model_file=\"/home/phillab/models/cls_roberta-base_factcc_first_0_f1_0.4766.bin\"), \"sign\": 1, \"only_doc\": True},\n", 73 | " {\"name\": \"DAE\", \"model\": BaselineScorer(model=\"dae\"), \"only_doc\": True, \"sign\": 1},\n", 74 | " {\"name\": \"FEQA\", \"model\": BaselineScorer(model=\"feqa\"), \"only_doc\": True, \"sign\": 1},\n", 75 | " {\"name\": \"QuestEval\", \"model\": BaselineScorer(model=\"questeval\"), \"only_doc\": True, \"sign\": 1},\n", 76 | " {\"name\": \"SummaC-ZS-VITC-L\", \"model\": SummaCZS(granularity=\"sentence\", model_name=\"vitc\", imager_load_cache=use_cache), \"sign\": 1, \"only_doc\": True},\n", 77 | " {\"name\": \"SummaC-Histo-VITC-L\", \"model\": SummaCConv(models=[\"vitc\"], granularity=\"sentence\", start_file=\"/home/phillab/models/summac/vitc_sentence_percentile_e_bacc0.744.bin\", bins=\"percentile\", imager_load_cache=use_cache, device=\"cpu\"), \"sign\": 1, \"only_doc\": True},\n", 78 | "]\n", 79 | "\n", 80 | "scorer_doc = ScorerWrapper(scorers, scoring_method=\"sum\", max_batch_size=20, use_caching=True)\n", 81 | "scorer_para = ScorerWrapper([s for s in scorers if \"only_doc\" not in s], scoring_method=\"sum\", max_batch_size=20, use_caching=True)" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 3, 87 | "metadata": { 88 | "scrolled": false 89 | }, 90 | "outputs": [ 91 | { 92 | "name": "stderr", 93 | "output_type": "stream", 94 | "text": [ 95 | "\r", 96 | " 0%| | 0/400 [00:00\n", 248 | " #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow0_col0 {\n", 249 | " : ;\n", 250 | " background-color: #9ece9e;\n", 251 | " color: #000000;\n", 252 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow0_col1 {\n", 253 | " : ;\n", 254 | " background-color: #ebf3eb;\n", 255 | " color: #000000;\n", 256 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow0_col2 {\n", 257 | " : ;\n", 258 | " background-color: #a5d1a5;\n", 259 | " color: #000000;\n", 260 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow0_col3 {\n", 261 | " : ;\n", 262 | " background-color: #9dcd9d;\n", 263 | " color: #000000;\n", 264 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow0_col4 {\n", 265 | " : ;\n", 266 | " background-color: #a5d1a5;\n", 267 | " color: #000000;\n", 268 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow0_col5 {\n", 269 | " : ;\n", 270 | " background-color: #e7f1e7;\n", 271 | " color: #000000;\n", 272 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow0_col6 {\n", 273 | " : ;\n", 274 | " background-color: #b4d8b4;\n", 275 | " color: #000000;\n", 276 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow1_col0 {\n", 277 | " : ;\n", 278 | " background-color: #acd4ac;\n", 279 | " color: #000000;\n", 280 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow1_col1 {\n", 281 | " : ;\n", 282 | " background-color: #c4e0c4;\n", 283 | " color: #000000;\n", 284 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow1_col2 {\n", 285 | " : ;\n", 286 | " background-color: #c5e0c5;\n", 287 | " color: #000000;\n", 288 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow1_col3 {\n", 289 | " : ;\n", 290 | " background-color: #e0eee0;\n", 291 | " color: #000000;\n", 292 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow1_col4 {\n", 293 | " : ;\n", 294 | " background-color: #ebf3eb;\n", 295 | " color: #000000;\n", 296 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow1_col5 {\n", 297 | " : ;\n", 298 | " background-color: #b9dbb9;\n", 299 | " color: #000000;\n", 300 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow1_col6 {\n", 301 | " : ;\n", 302 | " background-color: #d6e9d6;\n", 303 | " color: #000000;\n", 304 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow2_col0 {\n", 305 | " : ;\n", 306 | " background-color: #ebf3eb;\n", 307 | " color: #000000;\n", 308 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow2_col1 {\n", 309 | " : ;\n", 310 | " background-color: #94c994;\n", 311 | " color: #000000;\n", 312 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow2_col2 {\n", 313 | " : ;\n", 314 | " background-color: #ebf3eb;\n", 315 | " color: #000000;\n", 316 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow2_col3 {\n", 317 | " : ;\n", 318 | " background-color: #ebf3eb;\n", 319 | " color: #000000;\n", 320 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow2_col4 {\n", 321 | " : ;\n", 322 | " background-color: #dfeddf;\n", 323 | " color: #000000;\n", 324 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow2_col5 {\n", 325 | " : ;\n", 326 | " background-color: #ebf3eb;\n", 327 | " color: #000000;\n", 328 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow2_col6 {\n", 329 | " : ;\n", 330 | " background-color: #ebf3eb;\n", 331 | " color: #000000;\n", 332 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow3_col0 {\n", 333 | " : ;\n", 334 | " background-color: #a3d0a3;\n", 335 | " color: #000000;\n", 336 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow3_col1 {\n", 337 | " : ;\n", 338 | " background-color: #96c996;\n", 339 | " color: #000000;\n", 340 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow3_col2 {\n", 341 | " font-weight: bold;\n", 342 | " background-color: #75b975;\n", 343 | " color: #000000;\n", 344 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow3_col3 {\n", 345 | " : ;\n", 346 | " background-color: #badbba;\n", 347 | " color: #000000;\n", 348 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow3_col4 {\n", 349 | " : ;\n", 350 | " background-color: #9bcc9b;\n", 351 | " color: #000000;\n", 352 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow3_col5 {\n", 353 | " font-weight: bold;\n", 354 | " background-color: #76ba76;\n", 355 | " color: #000000;\n", 356 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow3_col6 {\n", 357 | " : ;\n", 358 | " background-color: #94c994;\n", 359 | " color: #000000;\n", 360 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow4_col0 {\n", 361 | " : ;\n", 362 | " background-color: #97ca97;\n", 363 | " color: #000000;\n", 364 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow4_col1 {\n", 365 | " font-weight: bold;\n", 366 | " background-color: #75b975;\n", 367 | " color: #000000;\n", 368 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow4_col2 {\n", 369 | " : ;\n", 370 | " background-color: #a6d1a6;\n", 371 | " color: #000000;\n", 372 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow4_col3 {\n", 373 | " font-weight: bold;\n", 374 | " background-color: #75b975;\n", 375 | " color: #000000;\n", 376 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow4_col4 {\n", 377 | " font-weight: bold;\n", 378 | " background-color: #75b975;\n", 379 | " color: #000000;\n", 380 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow4_col5 {\n", 381 | " : ;\n", 382 | " background-color: #78bb78;\n", 383 | " color: #000000;\n", 384 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow4_col6 {\n", 385 | " font-weight: bold;\n", 386 | " background-color: #75b975;\n", 387 | " color: #000000;\n", 388 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow5_col0 {\n", 389 | " font-weight: bold;\n", 390 | " background-color: #75b975;\n", 391 | " color: #000000;\n", 392 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow5_col1 {\n", 393 | " : ;\n", 394 | " background-color: #b2d7b1;\n", 395 | " color: #000000;\n", 396 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow5_col2 {\n", 397 | " : ;\n", 398 | " background-color: #a9d3a9;\n", 399 | " color: #000000;\n", 400 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow5_col3 {\n", 401 | " : ;\n", 402 | " background-color: #86c286;\n", 403 | " color: #000000;\n", 404 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow5_col4 {\n", 405 | " : ;\n", 406 | " background-color: #82c082;\n", 407 | " color: #000000;\n", 408 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow5_col5 {\n", 409 | " : ;\n", 410 | " background-color: #86c286;\n", 411 | " color: #000000;\n", 412 | " } #T_a290bb2c_3a8a_11ec_b554_d587d49477ferow5_col6 {\n", 413 | " : ;\n", 414 | " background-color: #84c084;\n", 415 | " color: #000000;\n", 416 | " }\n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | "
Balanced Accuracy
cogensumm xsumfaith polytope factcc summeval frank overall
model_name input
DAEdoc0.6340.5080.6280.7590.7030.6170.642
FEQAdoc0.610.560.5780.5360.5380.6990.587
NERdoc0.5020.6230.5170.50.5680.6090.553
QuestEvaldoc0.6260.6210.7030.6660.7250.8210.694
SummaC-Histo-VITC-Ldoc0.6470.6640.6270.8950.8170.8160.744
SummaC-ZS-VITC-Ldoc0.7040.5840.620.8380.7870.790.721
" 484 | ], 485 | "text/plain": [ 486 | "" 487 | ] 488 | }, 489 | "execution_count": 4, 490 | "metadata": {}, 491 | "output_type": "execute_result" 492 | } 493 | ], 494 | "source": [ 495 | "cm = sns.light_palette(\"green\", as_cmap=True)\n", 496 | "\n", 497 | "def highlight_max(data):\n", 498 | " is_max = data == data.max()\n", 499 | " return ['font-weight: bold' if v else '' for v in is_max]\n", 500 | "\n", 501 | "df = pd.DataFrame(results)\n", 502 | "df = df.groupby([\"model_name\", \"input\"]).agg({\"%s_bacc\" % (d): \"mean\" for d in benchmark.task_name_to_task})\n", 503 | "df.rename(columns={k: k.replace(\"_bacc\", \"\") for k in df.keys()}, inplace=True)\n", 504 | "df.drop(\"total\",inplace=True)\n", 505 | "df[\"overall\"] = (df[\"factcc\"]+df[\"frank\"]+df[\"polytope\"]+df[\"cogensumm\"]+df[\"summeval\"]+df[\"xsumfaith\"]) / (6.0)\n", 506 | "\n", 507 | "df.style.apply(highlight_max).background_gradient(cmap=cm, high=1.0, low=0.0).set_precision(3).set_caption(\"Balanced Accuracy\")" 508 | ] 509 | }, 510 | { 511 | "cell_type": "code", 512 | "execution_count": 16, 513 | "metadata": {}, 514 | "outputs": [ 515 | { 516 | "name": "stdout", 517 | "output_type": "stream", 518 | "text": [ 519 | "DATASET NAME MODEL NAME \n", 520 | "cogensumm DAE - 0.634 (0.598 - 0.677) (0.594 - 0.688)\n", 521 | "--------------\n", 522 | "cogensumm SummaC-ZS-VITC-L - 0.704 (0.668 - 0.745) (0.654 - 0.749)\n", 523 | "cogensumm SummaC-Histo-VITC-L - 0.647 (0.618 - 0.680) (0.612 - 0.684)\n", 524 | "==================================================\n", 525 | "xsumfaith NER - 0.623 (0.610 - 0.640) (0.607 - 0.644)\n", 526 | "--------------\n", 527 | "xsumfaith SummaC-ZS-VITC-L - 0.584 (0.561 - 0.606) (0.553 - 0.614)\n", 528 | "xsumfaith SummaC-Histo-VITC-L - 0.664 (0.643 - 0.694) (0.638 - 0.704)\n", 529 | "Significant difference (p < 0.05)\n", 530 | "==================================================\n", 531 | "polytope QuestEval - 0.703 (0.672 - 0.742) (0.657 - 0.745)\n", 532 | "--------------\n", 533 | "polytope SummaC-ZS-VITC-L - 0.620 (0.570 - 0.667) (0.557 - 0.684)\n", 534 | "polytope SummaC-Histo-VITC-L - 0.627 (0.552 - 0.680) (0.547 - 0.690)\n", 535 | "==================================================\n", 536 | "factcc DAE - 0.759 (0.720 - 0.797) (0.708 - 0.808)\n", 537 | "--------------\n", 538 | "factcc SummaC-ZS-VITC-L - 0.838 (0.809 - 0.870) (0.803 - 0.880)\n", 539 | "Significant difference (p < 0.05)\n", 540 | "factcc SummaC-Histo-VITC-L - 0.895 (0.878 - 0.916) (0.875 - 0.926)\n", 541 | "Significant difference (p < 0.05)\n", 542 | "Significant difference (p < 0.01)\n", 543 | "==================================================\n", 544 | "summeval QuestEval - 0.725 (0.702 - 0.758) (0.697 - 0.764)\n", 545 | "--------------\n", 546 | "summeval SummaC-ZS-VITC-L - 0.787 (0.755 - 0.823) (0.748 - 0.829)\n", 547 | "summeval SummaC-Histo-VITC-L - 0.817 (0.793 - 0.851) (0.788 - 0.858)\n", 548 | "Significant difference (p < 0.05)\n", 549 | "Significant difference (p < 0.01)\n", 550 | "==================================================\n", 551 | "frank QuestEval - 0.821 (0.809 - 0.835) (0.808 - 0.836)\n", 552 | "--------------\n", 553 | "frank SummaC-ZS-VITC-L - 0.790 (0.776 - 0.803) (0.775 - 0.807)\n", 554 | "frank SummaC-Histo-VITC-L - 0.816 (0.804 - 0.827) (0.801 - 0.830)\n", 555 | "==================================================\n", 556 | "==========================\n", 557 | "==========================\n", 558 | "==========================\n", 559 | "OVERALL QuestEval - (0.684 - 0.709) (0.682 - 0.711)\n", 560 | "OVERALL SummaC-ZS-VITC-L - (0.709 - 0.735) (0.707 - 0.737)\n", 561 | "OVERALL SummaC-Histo-VITC-L - (0.734 - 0.757) (0.730 - 0.760)\n" 562 | ] 563 | } 564 | ], 565 | "source": [ 566 | "# Analysis with confidence interval\n", 567 | "strongest_baseline = {\"cogensumm\": \"DAE\", \"xsumfaith\": \"NER\", \"polytope\": \"QuestEval\", \"factcc\": \"DAE\", \"summeval\": \"QuestEval\", \"frank\": \"QuestEval\"}\n", 568 | "\n", 569 | "P5 = 5 / 2 # Correction due to the fact that we are running 2 tests with the same data\n", 570 | "P1 = 1 / 2 # Correction due to the fact that we are running 2 tests with the same data\n", 571 | "\n", 572 | "def resample_balanced_acc(preds, labels, n_samples=100, sample_ratio=0.7):\n", 573 | " N = len(preds)\n", 574 | " idxs = list(range(N))\n", 575 | " N_batch = int(sample_ratio*N)\n", 576 | "\n", 577 | " bal_accs = []\n", 578 | " for _ in range(n_samples):\n", 579 | " random.shuffle(idxs)\n", 580 | " batch_preds = [preds[i] for i in idxs[:N_batch]]\n", 581 | " batch_labels = [labels[i] for i in idxs[:N_batch]]\n", 582 | " \n", 583 | " bal_accs.append(sklearn.metrics.balanced_accuracy_score(batch_labels, batch_preds))\n", 584 | " return bal_accs\n", 585 | "\n", 586 | "print(\"DATASET NAME\".ljust(15), \"MODEL NAME\".ljust(20))\n", 587 | "\n", 588 | "sampled_batch_preds = {res[\"model_name\"]: [] for res in results}\n", 589 | "for res in results:\n", 590 | " if res[\"model_name\"] == \"total\":\n", 591 | " print(\"==================================================\")\n", 592 | " continue\n", 593 | " \n", 594 | " samples = resample_balanced_acc(res[\"preds\"], res[\"labels\"])\n", 595 | " sampled_batch_preds[res[\"model_name\"]].append(samples)\n", 596 | " low5, high5 = np.percentile(samples, P5), np.percentile(samples, 100-P5)\n", 597 | " low1, high1 = np.percentile(samples, P1), np.percentile(samples, 100-P1)\n", 598 | " bacc = sklearn.metrics.balanced_accuracy_score(res[\"labels\"], res[\"preds\"])\n", 599 | " if \"SummaC\" in res[\"model_name\"] or res[\"model_name\"] == strongest_baseline[res[\"dataset_name\"]]:\n", 600 | " \n", 601 | " print(res[\"dataset_name\"].ljust(15), res[\"model_name\"].ljust(20), \" - %.3f (%.3f - %.3f) (%.3f - %.3f)\" % (bacc, low5, high5, low1, high1))\n", 602 | " if res[\"model_name\"] == strongest_baseline[res[\"dataset_name\"]]:\n", 603 | " bl5, bh5, bl1, bh1 = low5, high5, low1, high1\n", 604 | " print(\"--------------\")\n", 605 | " else:\n", 606 | " if low5 >= bh5:\n", 607 | " print(\"Significant difference (p < 0.05)\")\n", 608 | " if low1 >= bh1:\n", 609 | " print(\"Significant difference (p < 0.01)\")\n", 610 | "\n", 611 | "print(\"==========================\")\n", 612 | "print(\"==========================\")\n", 613 | "print(\"==========================\")\n", 614 | "\n", 615 | "baseline = np.mean(np.array(sampled_batch_preds[\"QuestEval\"]), axis=0)\n", 616 | "summaczs = np.mean(np.array(sampled_batch_preds[\"SummaC-ZS-VITC-L\"]), axis=0)\n", 617 | "summacconv = np.mean(np.array(sampled_batch_preds[\"SummaC-Histo-VITC-L\"]), axis=0)\n", 618 | "\n", 619 | "for model in [\"QuestEval\", \"SummaC-ZS-VITC-L\", \"SummaC-Histo-VITC-L\"]:\n", 620 | " samples = np.mean(np.array(sampled_batch_preds[model]), axis=0)\n", 621 | " low5, high5 = np.percentile(samples, P5), np.percentile(samples, 100-P5)\n", 622 | " low1, high1 = np.percentile(samples, P1), np.percentile(samples, 100-P1)\n", 623 | " \n", 624 | " print(\"OVERALL\".ljust(15), model.ljust(20), \" - (%.3f - %.3f) (%.3f - %.3f)\" % (low5, high5, low1, high1))" 625 | ] 626 | }, 627 | { 628 | "cell_type": "markdown", 629 | "metadata": {}, 630 | "source": [ 631 | "## ROC AUC score" 632 | ] 633 | }, 634 | { 635 | "cell_type": "code", 636 | "execution_count": 6, 637 | "metadata": {}, 638 | "outputs": [ 639 | { 640 | "data": { 641 | "text/html": [ 642 | "\n", 812 | " \n", 813 | " \n", 814 | " \n", 815 | " \n", 816 | " \n", 817 | " \n", 818 | " \n", 819 | " \n", 820 | " \n", 821 | " \n", 822 | " \n", 823 | " \n", 824 | " \n", 825 | " \n", 826 | " \n", 827 | " \n", 828 | " \n", 829 | " \n", 830 | " \n", 831 | " \n", 832 | " \n", 833 | " \n", 834 | " \n", 835 | " \n", 836 | " \n", 837 | " \n", 838 | " \n", 839 | " \n", 840 | " \n", 841 | " \n", 842 | " \n", 843 | " \n", 844 | " \n", 845 | " \n", 846 | " \n", 847 | " \n", 848 | " \n", 849 | " \n", 850 | " \n", 851 | " \n", 852 | " \n", 853 | " \n", 854 | " \n", 855 | " \n", 856 | " \n", 857 | " \n", 858 | " \n", 859 | " \n", 860 | " \n", 861 | " \n", 862 | " \n", 863 | " \n", 864 | " \n", 865 | " \n", 866 | " \n", 867 | " \n", 868 | " \n", 869 | " \n", 870 | " \n", 871 | " \n", 872 | " \n", 873 | " \n", 874 | " \n", 875 | " \n", 876 | " \n", 877 | " \n", 878 | "
ROC AUC
cogensumm xsumfaith polytope factcc summeval frank overall
model_name input
DAEdoc0.6780.4130.6410.8270.7740.6430.663
FEQAdoc0.6080.5340.5460.5070.5220.7480.577
NERdoc0.5020.6230.5170.50.5680.6090.553
QuestEvaldoc0.6440.6640.7220.7150.790.8790.736
SummaC-Histo-VITC-Ldoc0.6760.7020.6240.9220.860.8840.778
SummaC-ZS-VITC-Ldoc0.7310.580.6030.8370.8550.8530.743
" 879 | ], 880 | "text/plain": [ 881 | "" 882 | ] 883 | }, 884 | "execution_count": 6, 885 | "metadata": {}, 886 | "output_type": "execute_result" 887 | } 888 | ], 889 | "source": [ 890 | "df = pd.DataFrame(results)\n", 891 | "df = df.groupby([\"model_name\", \"input\"]).agg({\"%s_roc_auc\" % (d): \"mean\" for d in benchmark.task_name_to_task})\n", 892 | "df.rename(columns={k: k.replace(\"_roc_auc\", \"\") for k in df.keys()}, inplace=True)\n", 893 | "df.drop(\"total\",inplace=True)\n", 894 | "df[\"overall\"] = (df[\"factcc\"]+df[\"frank\"]+df[\"polytope\"]+df[\"cogensumm\"]+df[\"summeval\"]+df[\"xsumfaith\"]) / (6.0)\n", 895 | "\n", 896 | "df.style.apply(highlight_max).background_gradient(cmap=cm, high=1.0, low=0.0).set_precision(3).set_caption(\"ROC AUC\")" 897 | ] 898 | }, 899 | { 900 | "cell_type": "code", 901 | "execution_count": 17, 902 | "metadata": {}, 903 | "outputs": [ 904 | { 905 | "name": "stdout", 906 | "output_type": "stream", 907 | "text": [ 908 | "DATASET NAME MODEL NAME \n", 909 | "cogensumm DAE - 0.678 (0.639 - 0.726) (0.632 - 0.735)\n", 910 | "--------------\n", 911 | "cogensumm SummaC-ZS-VITC-L - 0.731 (0.697 - 0.767) (0.685 - 0.778)\n", 912 | "cogensumm SummaC-Histo-VITC-L - 0.676 (0.633 - 0.716) (0.627 - 0.720)\n", 913 | "==================================================\n", 914 | "xsumfaith QuestEval - 0.664 (0.631 - 0.688) (0.626 - 0.699)\n", 915 | "--------------\n", 916 | "xsumfaith SummaC-ZS-VITC-L - 0.580 (0.552 - 0.615) (0.547 - 0.616)\n", 917 | "xsumfaith SummaC-Histo-VITC-L - 0.702 (0.675 - 0.733) (0.666 - 0.740)\n", 918 | "==================================================\n", 919 | "polytope QuestEval - 0.722 (0.683 - 0.762) (0.682 - 0.766)\n", 920 | "--------------\n", 921 | "polytope SummaC-ZS-VITC-L - 0.603 (0.529 - 0.667) (0.524 - 0.685)\n", 922 | "polytope SummaC-Histo-VITC-L - 0.624 (0.560 - 0.679) (0.530 - 0.696)\n", 923 | "==================================================\n", 924 | "factcc DAE - 0.827 (0.793 - 0.863) (0.787 - 0.881)\n", 925 | "--------------\n", 926 | "factcc SummaC-ZS-VITC-L - 0.837 (0.800 - 0.879) (0.786 - 0.891)\n", 927 | "factcc SummaC-Histo-VITC-L - 0.922 (0.899 - 0.945) (0.895 - 0.952)\n", 928 | "Significant difference (p < 0.05)\n", 929 | "Significant difference (p < 0.01)\n", 930 | "==================================================\n", 931 | "summeval QuestEval - 0.790 (0.751 - 0.836) (0.750 - 0.843)\n", 932 | "--------------\n", 933 | "summeval SummaC-ZS-VITC-L - 0.855 (0.829 - 0.879) (0.817 - 0.887)\n", 934 | "summeval SummaC-Histo-VITC-L - 0.860 (0.837 - 0.883) (0.832 - 0.886)\n", 935 | "Significant difference (p < 0.05)\n", 936 | "==================================================\n", 937 | "frank QuestEval - 0.879 (0.870 - 0.888) (0.868 - 0.892)\n", 938 | "--------------\n", 939 | "frank SummaC-ZS-VITC-L - 0.853 (0.842 - 0.865) (0.839 - 0.867)\n", 940 | "frank SummaC-Histo-VITC-L - 0.884 (0.875 - 0.895) (0.869 - 0.898)\n", 941 | "==================================================\n", 942 | "==========================\n", 943 | "==========================\n", 944 | "==========================\n", 945 | "OVERALL QuestEval - (0.721 - 0.750) (0.715 - 0.751)\n", 946 | "OVERALL SummaC-ZS-VITC-L - (0.723 - 0.756) (0.719 - 0.764)\n", 947 | "OVERALL SummaC-Histo-VITC-L - (0.763 - 0.791) (0.762 - 0.792)\n" 948 | ] 949 | } 950 | ], 951 | "source": [ 952 | "# Analysis with confidence interval\n", 953 | "strongest_baseline = {\"cogensumm\": \"DAE\", \"xsumfaith\": \"QuestEval\", \"polytope\": \"QuestEval\", \"factcc\": \"DAE\", \"summeval\": \"QuestEval\", \"frank\": \"QuestEval\"}\n", 954 | "\n", 955 | "P5 = 5 / 2 # Correction due to the fact that we are running 2 tests with the same data\n", 956 | "P1 = 1 / 2 # Correction due to the fact that we are running 2 tests with the same data\n", 957 | "\n", 958 | "def resample_roc_auc(scores, labels, n_samples=100, sample_ratio=0.7):\n", 959 | " N = len(scores)\n", 960 | " idxs = list(range(N))\n", 961 | " N_batch = int(sample_ratio*N)\n", 962 | "\n", 963 | " roc_aucs = []\n", 964 | " for _ in range(n_samples):\n", 965 | " random.shuffle(idxs)\n", 966 | " batch_scores = [scores[i] for i in idxs[:N_batch]]\n", 967 | " batch_labels = [labels[i] for i in idxs[:N_batch]]\n", 968 | " roc_aucs.append(sklearn.metrics.roc_auc_score(batch_labels, batch_scores))\n", 969 | " return roc_aucs\n", 970 | "\n", 971 | "sampled_batch_preds = {res[\"model_name\"]: [] for res in results}\n", 972 | "print(\"DATASET NAME\".ljust(15), \"MODEL NAME\".ljust(20))\n", 973 | "for res in results:\n", 974 | " if res[\"model_name\"] == \"total\":\n", 975 | " print(\"==================================================\")\n", 976 | " continue\n", 977 | " samples = resample_roc_auc(res[\"scores\"], res[\"labels\"])\n", 978 | " sampled_batch_preds[res[\"model_name\"]].append(samples)\n", 979 | " low5, high5 = np.percentile(samples, P5), np.percentile(samples, 100-P5)\n", 980 | " low1, high1 = np.percentile(samples, P1), np.percentile(samples, 100-P1)\n", 981 | " roc_auc = sklearn.metrics.roc_auc_score(res[\"labels\"], res[\"scores\"])\n", 982 | " if \"SummaC\" in res[\"model_name\"] or res[\"model_name\"] == strongest_baseline[res[\"dataset_name\"]]:\n", 983 | " print(res[\"dataset_name\"].ljust(15), res[\"model_name\"].ljust(20), \" - %.3f (%.3f - %.3f) (%.3f - %.3f)\" % (roc_auc, low5, high5, low1, high1))\n", 984 | " if res[\"model_name\"] == strongest_baseline[res[\"dataset_name\"]]:\n", 985 | " bl5, bh5, bl1, bh1 = low5, high5, low1, high1\n", 986 | " print(\"--------------\")\n", 987 | " else:\n", 988 | " if low5 >= bh5:\n", 989 | " print(\"Significant difference (p < 0.05)\")\n", 990 | " if low1 >= bh1:\n", 991 | " print(\"Significant difference (p < 0.01)\")\n", 992 | "\n", 993 | "print(\"==========================\")\n", 994 | "print(\"==========================\")\n", 995 | "print(\"==========================\")\n", 996 | "\n", 997 | "baseline = np.mean(np.array(sampled_batch_preds[\"QuestEval\"]), axis=0)\n", 998 | "summaczs = np.mean(np.array(sampled_batch_preds[\"SummaC-ZS-VITC-L\"]), axis=0)\n", 999 | "summacconv = np.mean(np.array(sampled_batch_preds[\"SummaC-Histo-VITC-L\"]), axis=0)\n", 1000 | "\n", 1001 | "for model in [\"QuestEval\", \"SummaC-ZS-VITC-L\", \"SummaC-Histo-VITC-L\"]:\n", 1002 | " samples = np.mean(np.array(sampled_batch_preds[model]), axis=0)\n", 1003 | " low5, high5 = np.percentile(samples, P5), np.percentile(samples, 100-P5)\n", 1004 | " low1, high1 = np.percentile(samples, P1), np.percentile(samples, 100-P1)\n", 1005 | " \n", 1006 | " print(\"OVERALL\".ljust(15), model.ljust(20), \" - (%.3f - %.3f) (%.3f - %.3f)\" % (low5, high5, low1, high1))" 1007 | ] 1008 | } 1009 | ], 1010 | "metadata": { 1011 | "finalized": { 1012 | "timestamp": 1625710190289, 1013 | "trusted": true 1014 | }, 1015 | "interpreter": { 1016 | "hash": "c723e9d1a9662a11de23e7914e75631adbad784f516bc181f2c98a6790ee4bb2" 1017 | }, 1018 | "kernelspec": { 1019 | "display_name": "Python 3", 1020 | "language": "python", 1021 | "name": "python3" 1022 | }, 1023 | "language_info": { 1024 | "codemirror_mode": { 1025 | "name": "ipython", 1026 | "version": 3 1027 | }, 1028 | "file_extension": ".py", 1029 | "mimetype": "text/x-python", 1030 | "name": "python", 1031 | "nbconvert_exporter": "python", 1032 | "pygments_lexer": "ipython3", 1033 | "version": "3.7.6" 1034 | } 1035 | }, 1036 | "nbformat": 4, 1037 | "nbformat_minor": 4 1038 | } 1039 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | torch 2 | transformers>=4.8.1 3 | nltk>=3.6.6 4 | huggingface-hub<=0.28.0 # Updated 5 | sentencepiece 6 | protobuf 7 | -------------------------------------------------------------------------------- /script.sh: -------------------------------------------------------------------------------- 1 | python run_summac_precomp.py --model vitc --granularity sentence 2 | python train_summac.py --model vitc --granularity sentence --train_batch_size 16 --num_epochs 10 --nli_labels e 3 | 4 | 5 | # python run_summac_precomp.py --model mnli-base --granularity paragraph 6 | # python run_summac_precomp.py --model mnli-base --granularity 2sents 7 | 8 | # python train_summac.py --model mnli-base --granularity paragraph --train_batch_size 8 --nli_labels e 9 | # python train_summac.py --model mnli-base --granularity 2sents --train_batch_size 8 --nli_labels e 10 | 11 | 12 | # python train_summac.py --model mnli --granularity sentence --train_batch_size 8 --nli_labels n 13 | # python train_summac.py --model mnli --granularity sentence --train_batch_size 8 --nli_labels ec 14 | # python train_summac.py --model mnli --granularity sentence --train_batch_size 8 --nli_labels en 15 | # python train_summac.py --model mnli --granularity sentence --train_batch_size 8 --nli_labels cn 16 | # python train_summac.py --model mnli --granularity sentence --train_batch_size 8 --nli_labels ecn 17 | 18 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels e 19 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels c 20 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels n 21 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels ec 22 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels en 23 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels cn 24 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels ecn 25 | 26 | 27 | # python train_summac.py --model anli --granularity sentence --train_batch_size 8 --nli_labels e 28 | # python train_summac.py --model anli --granularity sentence --train_batch_size 8 --nli_labels c 29 | # python train_summac.py --model anli --granularity sentence --train_batch_size 8 --nli_labels n 30 | # python train_summac.py --model anli --granularity sentence --train_batch_size 8 --nli_labels ec 31 | # python train_summac.py --model anli --granularity sentence --train_batch_size 8 --nli_labels en 32 | # python train_summac.py --model anli --granularity sentence --train_batch_size 8 --nli_labels cn 33 | # python train_summac.py --model anli --granularity sentence --train_batch_size 8 --nli_labels ecn 34 | 35 | # conda init bash 36 | 37 | # conda activate feqa 38 | # python run_baseline.py --model feqa --cut test 39 | # conda activate questeval 40 | # python run_baseline.py --model questeval --cut test 41 | # conda deactivate 42 | 43 | # python train_summac.py --model anli --bins even100 --granularity sentence --train_batch_size 8 --nli_labels e 44 | # python train_summac.py --model snli-base --bins even100 --granularity sentence --train_batch_size 8 --nli_labels e 45 | # python train_summac.py --model snli-large --bins even100 --granularity sentence --train_batch_size 8 --nli_labels e 46 | # python train_summac.py --model mnli-base --bins even100 --granularity sentence --train_batch_size 8 --nli_labels e 47 | # python train_summac.py --model vitc-base --bins even100 --granularity sentence --train_batch_size 8 --nli_labels e 48 | # python train_summac.py --model vitc-only --bins even100 --granularity sentence --train_batch_size 8 --nli_labels e 49 | 50 | # python run_summac_precomp.py --model decomp --granularity sentence 51 | # python train_summac.py --model decomp --granularity sentence --train_batch_size 8 --nli_labels e 52 | 53 | # python run_summac_precomp.py --model anli --granularity sentence 54 | # python run_summac_precomp.py --model snli-large --granularity sentence 55 | # python run_summac_precomp.py --model snli-base --granularity sentence 56 | # python run_summac_precomp.py --model mnli-base --granularity sentence 57 | # python run_summac_precomp.py --model vitc-base --granularity sentence 58 | # python run_summac_precomp.py --model vitc-only --granularity sentence 59 | 60 | # python train_summac.py --model vitc --bins even100 --granularity sentence --train_batch_size 8 --nli_labels c 61 | # python train_summac.py --model vitc --bins even100 --granularity sentence --train_batch_size 8 --nli_labels n 62 | # python train_summac.py --model vitc --bins even100 --granularity sentence --train_batch_size 8 --nli_labels ec 63 | # python train_summac.py --model vitc --bins even100 --granularity sentence --train_batch_size 8 --nli_labels en 64 | # python train_summac.py --model vitc --bins even100 --granularity sentence --train_batch_size 8 --nli_labels cn 65 | # python train_summac.py --model vitc --bins even100 --granularity sentence --train_batch_size 8 --nli_labels ecn 66 | 67 | 68 | 69 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels e 70 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels c 71 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels n 72 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels ec 73 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels en 74 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels cn 75 | # python train_summac.py --model vitc --granularity sentence --train_batch_size 8 --nli_labels ecn 76 | 77 | 78 | # python run_ec_precompute.py --model mnli --granularity 2sents 79 | # python run_ec_precompute.py --model vitc --granularity 2sents 80 | 81 | # python train_summac.py --model snli-large --granularity sentence 82 | # python train_summac.py --model snli-large --granularity sentence --bins even100 83 | # python train_summac.py --model snli-large --granularity paragraph 84 | # python train_summac.py --model snli-large --granularity paragraph --bins even100 85 | # python train_summac.py --model mnli-base --granularity sentence 86 | # python train_summac.py --model mnli-base --granularity sentence --bins even100 87 | # python train_summac.py --model mnli-base --granularity paragraph 88 | # python train_summac.py --model mnli-base --granularity paragraph --bins even100 89 | # python train_summac.py --model mnli --granularity sentence 90 | # python train_summac.py --model mnli --granularity sentence --bins even100 91 | # python train_summac.py --model mnli --granularity paragraph 92 | # python train_summac.py --model mnli --granularity paragraph --bins even100 93 | # python train_summac.py --model anli --granularity sentence 94 | # python train_summac.py --model anli --granularity sentence --bins even100 95 | # python train_summac.py --model anli --granularity paragraph 96 | # python train_summac.py --model anli --granularity paragraph --bins even100 97 | # python train_summac.py --model vitc-base --granularity sentence 98 | # python train_summac.py --model vitc-base --granularity sentence --bins even100 99 | # python train_summac.py --model vitc-base --granularity paragraph 100 | # python train_summac.py --model vitc-base --granularity paragraph --bins even100 101 | # python train_summac.py --model vitc --granularity sentence 102 | # python train_summac.py --model vitc --granularity sentence --bins even100 103 | # python train_summac.py --model vitc --granularity paragraph 104 | # python train_summac.py --model vitc --granularity paragraph --bins even100 105 | # python train_summac.py --model vitc-only --granularity sentence 106 | # python train_summac.py --model vitc-only --granularity sentence --bins even100 107 | # python train_summac.py --model vitc-only --granularity paragraph 108 | # python train_summac.py --model vitc-only --granularity paragraph --bins even100 109 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | from pathlib import Path 3 | 4 | 5 | REQUIREMENTS_PATH = Path(__file__).resolve().parent / "requirements.txt" 6 | 7 | with open(str(REQUIREMENTS_PATH), "r", encoding="utf-8") as f: 8 | requirements = f.read().splitlines() 9 | 10 | setup( 11 | name="summac", 12 | packages=find_packages(include=["summac"]), 13 | version="0.0.4", 14 | license="Apache", 15 | long_description=open("README.md").read(), 16 | long_description_content_type="text/markdown", 17 | install_requires=requirements, 18 | # extras_require={}, 19 | include_package_data=True, 20 | ) 21 | -------------------------------------------------------------------------------- /summac/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tingofurro/summac/c1f3da93cd074c24d8033eb27a88b5a7cc5c08fa/summac/__init__.py -------------------------------------------------------------------------------- /summac/benchmark.py: -------------------------------------------------------------------------------- 1 | import json, os, pandas as pd, numpy as np, csv 2 | from datasets import load_dataset 3 | from collections import Counter 4 | import requests, zipfile, tarfile 5 | from .utils_scorer import choose_best_threshold 6 | from .utils_misc import download_file_from_google_drive 7 | from tqdm import tqdm 8 | from itertools import islice 9 | 10 | 11 | 12 | # SummaC Benchmark 13 | class SummaCBenchmark: 14 | 15 | def __init__(self, 16 | benchmark_folder="/home/phillab/data/summac_benchmark/", 17 | dataset_names=["cogensumm", "xsumfaith", "polytope", "factcc", "summeval", "frank"], 18 | cut="val", 19 | hf_datasets_cache_dir = os.environ.get("HF_DATASETS_CACHE", None), 20 | debug=False): 21 | assert cut in ["val", "test"], "Unrecognized cut for the Fact Checking Benchmark" 22 | if not os.path.exists(benchmark_folder): 23 | os.makedirs(benchmark_folder) 24 | 25 | self.cut = cut 26 | self.benchmark_folder = benchmark_folder 27 | self.cnndm_id2reference = None 28 | self.cnndm = None 29 | self.xsum = None 30 | self.hf_datasets_cache_dir = hf_datasets_cache_dir 31 | self.debug = debug 32 | 33 | self.datasets = [] 34 | for dataset_name in dataset_names: 35 | print ("Loading dataset %s" % (dataset_name)) 36 | if dataset_name == "cogensumm": 37 | self.load_cogensumm() 38 | elif dataset_name == "xsumfaith": 39 | self.load_xsumfaith() 40 | elif dataset_name == "polytope": 41 | self.load_polytope() 42 | elif dataset_name == "factcc": 43 | self.load_factcc() 44 | elif dataset_name == "summeval": 45 | self.load_summeval() 46 | elif dataset_name == "frank": 47 | self.load_frank() 48 | else: 49 | raise ValueError("Unrecognized dataset name: %s" % (dataset_name)) 50 | 51 | # Underlying dataset loader: CNN/DM and XSum 52 | def get_cnndm_document(self, aid): 53 | if self.cnndm is None: 54 | self.cnndm= load_dataset("cnn_dailymail", "3.0.0", cache_dir=self.hf_datasets_cache_dir) 55 | self.cnndm_id2article = {} 56 | for cut in ["test", "validation"]: 57 | self.cnndm_id2article.update({d["id"]: d["article"] for d in self.cnndm[cut]}) 58 | return self.cnndm_id2article[aid] 59 | 60 | def get_cnndm_reference(self, aid): 61 | if self.cnndm is None: 62 | self.cnndm= load_dataset("cnn_dailymail", "3.0.0", cache_dir=self.hf_datasets_cache_dir) 63 | if self.cnndm_id2reference is None: 64 | self.cnndm_id2reference = {} 65 | for cut in ["test", "validation"]: 66 | self.cnndm_id2reference.update({d["id"]: d["highlights"] for d in self.cnndm[cut]}) 67 | return self.cnndm_id2reference[aid] 68 | 69 | 70 | def get_xsum_document(self, aid): 71 | if self.xsum is None: 72 | self.xsum = load_dataset("xsum", cache_dir=self.hf_datasets_cache_dir, trust_remote_code=True)["test"] 73 | self.xsumid2article = {d["id"]: d["document"] for d in self.xsum} 74 | 75 | return self.xsumid2article[aid] 76 | 77 | # Individual dataset loaders 78 | def load_cogensumm(self): 79 | # Correctness of Generated Summaries: https://www.aclweb.org/anthology/P19-1213.pdf 80 | # CoGenSumm: https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2002 81 | 82 | dataset_folder = os.path.join(self.benchmark_folder, "cogensumm/") 83 | if not os.path.exists(dataset_folder): 84 | print("==== CoGenSumm dataset not found, downloading from scratch") 85 | os.makedirs(dataset_folder) 86 | data = requests.get("https://tudatalib.ulb.tu-darmstadt.de/bitstream/handle/tudatalib/2002/summary-correctness-v1.0.zip?sequence=3&isAllowed=y") 87 | zip_file = os.path.join(dataset_folder, "summary-correctness-v1.0.zip") 88 | with open(zip_file, "wb") as f: 89 | f.write(data.content) 90 | 91 | with zipfile.ZipFile(zip_file, "r") as zip_ref: 92 | zip_ref.extractall(dataset_folder) 93 | os.remove(zip_file) 94 | 95 | clean_dataset = [] 96 | for fn in os.listdir(dataset_folder): 97 | if self.cut not in fn: 98 | continue 99 | 100 | with open(os.path.join(dataset_folder, fn), "r") as f: 101 | dataset = json.load(f) 102 | 103 | if self.debug: 104 | if type(dataset) == dict: 105 | dataset = dict(islice(dataset.items(), 4)) 106 | elif type(dataset) == list: 107 | dataset = dataset[:4] 108 | 109 | if "_org" in fn or fn == "test_chen18_reranked.json": 110 | for aid in tqdm(dataset): 111 | document = self.get_cnndm_document(aid) 112 | label = 0 if dataset[aid]["label"] == "Incorrect" else 1 113 | sents = dataset[aid]["sents"] 114 | summary = " ".join([sents[str(i)]["text"] for i in range(len(sents))]) 115 | clean_dataset.append({"filename": fn, "label": label, "document": document, "claim": summary, "cnndm_id": aid, "annotations": [label], "dataset": "cogensumm", "origin": "cnndm"}) 116 | elif fn == "val_reranking.json": 117 | for aid in tqdm(dataset): 118 | document = self.get_cnndm_document(aid) 119 | for idx, data in dataset[aid].items(): 120 | label = 0 if data["label"] == "Incorrect" else 1 121 | summary = " ".join([data["sents"][str(i)]["text"] for i in range(len(data["sents"]))]) 122 | clean_dataset.append({"filename": fn, "label": label, "document": document, "claim": summary, "cnndm_id": aid, "annotations": [label], "dataset": "cogensumm", "origin": "cnndm"}) 123 | elif fn == "val_sentence_pairs.json": 124 | for d in tqdm(dataset): 125 | aid = d["article_id"] 126 | document = self.get_cnndm_document(aid) 127 | clean_dataset.append({"filename": fn, "label": 1, "document": document, "claim": d["correct_sent"], "cnndm_id": aid, "annotations": [1], "dataset": "cogensumm", "origin": "cnndm"}) 128 | clean_dataset.append({"filename": fn, "label": 0, "document": document, "claim": d["incorrect_sent"], "cnndm_id": aid, "annotations": [0], "dataset": "cogensumm", "origin": "cnndm"}) 129 | self.datasets.append({"name": "cogensumm", "dataset": clean_dataset}) 130 | 131 | def load_xsumfaith(self): 132 | # On Faithfulness and Factuality in Abstractive Summarization - ACL 2020 133 | # https://github.com/google-research-datasets/xsum_hallucination_annotations 134 | # https://aclanthology.org/2020.acl-main.173.pdf 135 | 136 | dataset_folder = os.path.join(self.benchmark_folder, "xsumfaith/") 137 | if not os.path.exists(dataset_folder): 138 | print("==== XSum dataset not found, downloading from scratch") 139 | os.makedirs(dataset_folder) 140 | 141 | csv_file = requests.get("https://github.com/google-research-datasets/xsum_hallucination_annotations/raw/master/hallucination_annotations_xsum_summaries.csv") 142 | with open(os.path.join(dataset_folder, "hallucination_annotations_xsum_summaries.csv"), "wb") as f: 143 | f.write(csv_file.content) 144 | 145 | path_to_annotation = os.path.join(dataset_folder, "hallucination_annotations_xsum_summaries.csv") 146 | 147 | with open(path_to_annotation, "r") as f: 148 | raw_data = list(csv.reader(f)) 149 | dataset = [] 150 | keys = raw_data[0] 151 | for line in raw_data[1:]: 152 | dataset.append({k: v for k, v in zip(keys, line)}) 153 | 154 | groups = {} 155 | for d in dataset: 156 | k = (d["bbcid"], d["system"]) 157 | if k not in groups: 158 | groups[k] = [] 159 | groups[k].append(d) 160 | 161 | clean_dataset = [] 162 | 163 | if self.debug: 164 | groups = dict(islice(groups.items(), 4)) 165 | for k, vs in tqdm(groups.items()): 166 | A = vs[0] 167 | document = self.get_xsum_document(A["bbcid"]) 168 | labels = [v["hallucination_type"] for v in vs] 169 | annotations = [1 if label == "NULL" else 0 for label in labels] 170 | most_common_label = Counter(labels).most_common(1)[0][0] 171 | label = 1 if most_common_label == "NULL" else 0 172 | c = "val" if len(clean_dataset) % 2 == 0 else "test" 173 | 174 | clean_dataset.append({"document": document, "claim": A["summary"], "bbcid": A["bbcid"], "model_name": A["system"], "label": label, "cut": c, "annotations": annotations, "dataset": "xsumfaith", "origin": "xsum"}) 175 | final_dataset = [d for d in clean_dataset if d["cut"]==self.cut] 176 | self.datasets.append({"name": "xsumfaith", "dataset": final_dataset}) 177 | 178 | def load_polytope(self, which_label="overall"): 179 | # What Have We Achieved on Text Summarization? [https://arxiv.org/abs/2010.04529] 180 | # Dataset must be downloaded from the Github repo: https://github.com/hddbang/polytope 181 | 182 | assert which_label in ["overall", "omission", "addition", "duplication", "inaccuracy"], "Unrecognized `which label`" 183 | 184 | dataset_folder = os.path.join(self.benchmark_folder, "polytope") 185 | if not os.path.exists(dataset_folder): 186 | print("==== Polytope dataset not found, downloading from scratch") 187 | os.makedirs(dataset_folder) 188 | 189 | for model_name in ["BART", "Bert_Ext", "Bert_Ext_Abs", "BottomUp", "PG", "PG_Coverage", "Summa", "TextRank", "seq2seq"]: 190 | url = "https://github.com/hddbang/PolyTope/raw/master/outputs_with_human_annotation/Human_Annotation_Summarization_%s.xlsm" % (model_name) 191 | r = requests.get(url) 192 | with open(os.path.join(dataset_folder, "Human_Annotation_Summarization_%s.xlsm" % (model_name)), "wb") as f: 193 | f.write(r.content) 194 | 195 | full_dataset = [] 196 | for fn in os.listdir(dataset_folder): 197 | fn = os.path.join(dataset_folder, fn) 198 | 199 | all_segments = pd.read_excel(fn, sheet_name="Scores per segment") 200 | ID2row = {} 201 | for i, segment in all_segments.iterrows(): 202 | c = "val" if i % 2 == 0 else "test" 203 | if str(segment["ID"]) != "nan": 204 | ID2row[segment["ID"]] = {"ID": segment["ID"], "document": segment["Source"], "claim": segment["Target"], "errors": [], "cut": c} 205 | 206 | for i, row in pd.read_excel(fn, sheet_name="Error Log").iterrows(): 207 | if str(row["Subtypes"]) != "nan": 208 | ID2row[row["ID"]]["errors"].append(row["Subtypes"]) 209 | 210 | if self.debug: 211 | ID2row = dict(islice(ID2row.items(), 4)) 212 | for ID in tqdm(ID2row): 213 | d = ID2row[ID] 214 | d["overall_label"] = 1 if len(d["errors"]) == 0 else 0 215 | d["omission_label"] = 0 if "Omission" in d["errors"] else 1 216 | d["addition_label"] = 0 if "Addition" in d["errors"] else 1 217 | d["duplication_label"] = 0 if "Duplication" in d["errors"] else 1 218 | d["inaccuracy_label"] = 0 if "Inaccuracy_internal" in d["errors"] or "Inaccuracy_external" in d["errors"] else 1 219 | if which_label is not None: 220 | d["label"] = d["%s_label" % (which_label)] 221 | d["dataset"] = "polytope" 222 | d["annotations"] = [d["label"]] 223 | d["origin"] = "cnndm" 224 | 225 | full_dataset.append(d) 226 | cut_dataset = [d for d in full_dataset if d["cut"]==self.cut] 227 | self.datasets.append({"name": "polytope", "dataset": cut_dataset}) 228 | 229 | def load_factcc(self, max_entries=-1): 230 | # Evaluating the Factual Consistency of Abstractive Text Summarization [https://arxiv.org/abs/1910.12840] 231 | # Dataset for each split must be downloaded from the Github repo: https://github.com/salesforce/factCC 232 | 233 | dataset_folder = os.path.join(self.benchmark_folder, "factcc/") 234 | if not os.path.exists(dataset_folder): 235 | print("==== FactCC dataset not found, downloading from scratch") 236 | os.makedirs(dataset_folder) 237 | 238 | urls = ["https://storage.googleapis.com/sfr-factcc-data-research/unpaired_generated_data.tar.gz", "https://storage.googleapis.com/sfr-factcc-data-research/unpaired_annotated_data.tar.gz"] 239 | for url in urls: 240 | zip_name = url.split("/")[-1] 241 | r = requests.get(url) 242 | with open(os.path.join(dataset_folder, zip_name), "wb") as f: 243 | f.write(r.content) 244 | 245 | with tarfile.open(os.path.join(dataset_folder, zip_name), "r:gz") as f: 246 | f.extractall(dataset_folder) 247 | os.remove(os.path.join(dataset_folder, zip_name)) 248 | 249 | if self.cut == "train": 250 | dataset = [] 251 | with open(os.path.join(dataset_folder, "unpaired_generated_data/data-original/data-train.jsonl"), "r") as f: 252 | for i, line in enumerate(f): 253 | if max_entries > 0 and i >= max_entries: 254 | break 255 | D = json.loads(line) 256 | aid = D["filepath"].split("/")[-1].replace(".story", "") 257 | full_text = self.get_cnndm_document(aid) 258 | 259 | label = 1 if D["label"]=="CORRECT" else 0 260 | datum = {"document": full_text, "claim": D["claim"], "cnndm_id": D["id"], "label": label, "dataset": "factcc", "origin": "cnndm"} 261 | dataset.append(datum) 262 | 263 | if self.cut in ["val", "test"]: 264 | factcc_file = os.path.join(dataset_folder, "unpaired_annotated_data/%s/data-dev.jsonl" % (self.cut)) 265 | dataset = [] 266 | with open(factcc_file, "r") as f: 267 | for line in f: 268 | dataset.append(json.loads(line)) 269 | 270 | if self.debug: 271 | dataset = dataset[:4] 272 | for d in tqdm(dataset): 273 | aid = d["filepath"].split("/")[-1].replace(".story", "") 274 | d["document"] = self.get_cnndm_document(aid) 275 | d["label"] = 1 if d["label"] == "CORRECT" else 0 276 | d["annotations"] = [d["label"]] 277 | d["dataset"] = "factcc" 278 | d["origin"] = "cnndm" 279 | 280 | self.datasets.append({"name": "factcc", "dataset": dataset}) 281 | 282 | def load_summeval(self, key_focus="consistency"): 283 | assert key_focus in ["consistency", "coherence", "fluency", "relevance"] 284 | # SummEval: Re-evaluating Summarization Evaluation [https://arxiv.org/abs/2007.12626] 285 | # Data files must be downloaded from the following Github repository: https://github.com/Yale-LILY/SummEval 286 | raw_dataset = [] 287 | 288 | dataset_folder = os.path.join(self.benchmark_folder, "summeval/") 289 | fn = os.path.join(dataset_folder, "model_annotations.aligned.scored.jsonl") 290 | if not os.path.exists(dataset_folder): 291 | print("==== SummEval dataset not found, downloading from scratch") 292 | os.makedirs(dataset_folder) 293 | 294 | # From the 4/19/2020 update on the README: https://github.com/Yale-LILY/SummEval 295 | download_file_from_google_drive("1d2Iaz3jNraURP1i7CfTqPIj8REZMJ3tS", fn) 296 | 297 | with open(fn, "r") as f: 298 | for line in f: 299 | raw_dataset.append(json.loads(line)) 300 | 301 | clean_dataset = [] 302 | 303 | if self.debug: 304 | raw_dataset = raw_dataset[:4] 305 | for i, d in enumerate(tqdm(raw_dataset)): 306 | c = "val" if i % 2 == 0 else "test" 307 | _, _, article_id = d["id"].split("-") 308 | document = self.get_cnndm_document(article_id) 309 | annotations = d["expert_annotations"] 310 | 311 | consistencies = [a[key_focus] for a in annotations] 312 | final_label = 1 if len([cons for cons in consistencies if cons==5]) > len(annotations)/2 else 0 313 | 314 | # annotations = [1 if cons == 5 else 0 for cons in consistencies] 315 | annotations = consistencies 316 | error_type = "no error" if final_label == 1 else "error" 317 | 318 | clean_dataset.append({"document": document, "claim": d["decoded"], "label": final_label, "model_name": d["model_id"], "cnndm_id": d["id"], "cut": c, "annotations": annotations, "dataset": "summeval", "origin": "cnndm", "error_type": error_type}) 319 | final_dataset = [d for d in clean_dataset if d["cut"] == self.cut] 320 | self.datasets.append({"name": "summeval", "dataset": final_dataset}) 321 | 322 | def load_frank(self): 323 | # FRANK: Factuality Evaluation Benchmark [https://aclanthology.org/2021.naacl-main.383.pdf] 324 | # Files must be downloaded from the Github repository: https://github.com/artidoro/frank 325 | 326 | dataset_folder = os.path.join(self.benchmark_folder, "frank/") 327 | if not os.path.exists(dataset_folder): 328 | print("==== Frank dataset not found, downloading from scratch") 329 | os.makedirs(dataset_folder) 330 | 331 | fns = ["human_annotations_sentence.json", "validation_split.txt", "test_split.txt"] 332 | for fn in fns: 333 | data = requests.get("https://raw.githubusercontent.com/artidoro/frank/main/data/%s" % fn) 334 | with open(os.path.join(dataset_folder, fn), "w") as f: 335 | f.write(data.text) 336 | 337 | raw_file = os.path.join(dataset_folder, "human_annotations_sentence.json") 338 | val_hash_file = os.path.join(dataset_folder, "validation_split.txt") 339 | test_hash_file = os.path.join(dataset_folder, "test_split.txt") 340 | with open(val_hash_file if self.cut=="val" else test_hash_file, "r") as f: 341 | valid_hashes = set([line.strip() for line in f]) 342 | 343 | with open(raw_file, "r") as f: 344 | raw_dataset = json.load(f) 345 | dataset = [] 346 | 347 | if self.debug: 348 | raw_dataset = raw_dataset[:100] 349 | for d in tqdm(raw_dataset): 350 | article = d["article"] 351 | origin = "cnndm" if len(d["hash"]) >= 40 else "xsum" 352 | 353 | if d["hash"] not in valid_hashes: 354 | continue 355 | 356 | summ_labels = [] 357 | annotator_labels = {} 358 | for annot in d["summary_sentences_annotations"]: 359 | annot_vals = [an for ans in annot.values() for an in ans] 360 | noerror_count = len([an for an in annot_vals if an=="NoE"]) 361 | label = 1 if noerror_count >= 2 else 0 362 | summ_labels.append(label) 363 | for anno_name, anno in annot.items(): 364 | if anno_name not in annotator_labels: 365 | annotator_labels[anno_name] = [] 366 | annotator_labels[anno_name] += anno 367 | 368 | annotations = [1 if all(a=="NoE" for a in annos) else 0 for annos in annotator_labels.values()] 369 | label = 0 if any(sl==0 for sl in summ_labels) else 1 370 | 371 | error_type = "NoE" 372 | if label == 0: 373 | errors = [anno for annos in annotator_labels.values() for anno in annos if anno != "NoE"] 374 | error_type = Counter(errors).most_common(1)[0][0] 375 | 376 | summary = d["summary"] 377 | dataset.append({"document": article, "claim": summary, "label": label, "cut": self.cut, "hash": d["hash"], "model_name": d["model_name"], "annotations": annotations, "dataset": "frank", "origin": origin, "error_type": error_type}) 378 | self.datasets.append({"name": "frank", "dataset": dataset}) 379 | 380 | def get_dataset(self, dataset_name): 381 | for dataset in self.datasets: 382 | if dataset["name"] == dataset_name: 383 | return dataset["dataset"] 384 | raise ValueError("Unrecognized dataset name: %s" % (dataset_name)) 385 | 386 | def print_stats(self): 387 | dataset_stats = [] 388 | for dataset in self.datasets: 389 | N_pos, N_neg = len([d for d in dataset["dataset"] if d["label"]==1]), len([d for d in dataset["dataset"] if d["label"]==0]) 390 | dataset_stats.append({"name": dataset["name"], "N": len(dataset["dataset"]), "N_pos": N_pos, "N_neg": N_neg, "frac_pos": N_pos/(N_pos+N_neg)}) 391 | print(pd.DataFrame(dataset_stats)) 392 | 393 | def evaluate(self, scorer): 394 | benchmark = [] 395 | 396 | for dataset in self.datasets: 397 | dataset_labels = [d["label"] for d in dataset["dataset"]] 398 | dataset_preds = scorer.score([d["document"] for d in dataset["dataset"]], [d["claim"] for d in dataset["dataset"]])["scores"] 399 | 400 | dataset_thresh, dataset_f1 = choose_best_threshold(dataset_labels, dataset_preds) 401 | benchmark.append({"name": dataset["name"], "score": dataset_f1, "threshold": dataset_thresh}) 402 | return {"overall_score": np.mean([t["score"] for t in benchmark]), "benchmark": benchmark} 403 | 404 | 405 | if __name__ == "__main__": 406 | import random 407 | 408 | for cut in ["val", "test"]: 409 | summac_benchmark = SummaCBenchmark(benchmark_folder="/home/tingu/data/summac_benchmark2/", cut=cut) 410 | print("============= SUMMAC %s ===============" % (cut.upper())) 411 | summac_benchmark.print_stats() 412 | for dataset in summac_benchmark.datasets: 413 | print("\n============= %s ===============" % (dataset["name"])) 414 | random.shuffle(dataset["dataset"]) 415 | print(dataset["dataset"][0]["document"][:400]) 416 | print("-------------") 417 | print(dataset["dataset"][0]["claim"]) 418 | print("-------------") 419 | print(dataset["dataset"][0]["label"]) 420 | -------------------------------------------------------------------------------- /summac/model_baseline.py: -------------------------------------------------------------------------------- 1 | import datasets, nltk, numpy as np 2 | import json, os, sys, argparse 3 | 4 | class BaselineScorer: 5 | def __init__(self, model="questeval", do_weighter=False, load_cache=True): 6 | assert model in ["questeval", "feqa", "dae"], "Unrecognized baseline model" 7 | self.model = model 8 | self.do_weighter = do_weighter 9 | self.model_loaded = False 10 | self.cache = {} 11 | self.cache_file = "/export/share/plaban/summac_cache/cache_%s.json" % (self.model) 12 | if load_cache: 13 | self.load_cache() 14 | 15 | def load_model(self): 16 | if self.model == "questeval": 17 | from questeval.questeval_metric import QuestEval 18 | self.questeval = QuestEval(isCuda=True, do_weighter=self.do_weighter) 19 | elif self.model == "feqa": 20 | # import benepar, nltk 21 | # benepar.download('benepar_en2') 22 | # nltk.download('stopwords') 23 | from feqa import FEQA 24 | self.scorer = FEQA(use_gpu=True) 25 | elif self.model == "dae": 26 | sys.path.insert(0, "/home/phillab/dae-factuality/") 27 | from evaluate_factuality import MODEL_CLASSES, score_example_single_context 28 | 29 | parser = argparse.ArgumentParser() 30 | args = parser.parse_args() 31 | args.device = "cuda:0" 32 | args.per_gpu_eval_batch_size = 8 33 | args.max_seq_length = 128 34 | args.dependency_type = "enhancedDependencies" 35 | self.args = args 36 | 37 | model_dir = "/home/phillab/models/dae_basic/" 38 | model_type = "electra_dae" 39 | config_class, model_class, tokenizer_class = MODEL_CLASSES[model_type] 40 | 41 | self.tokenizer = tokenizer_class.from_pretrained(model_dir) 42 | self.dae_model = model_class.from_pretrained(model_dir) 43 | self.dae_model.to(args.device) 44 | 45 | self.model_loaded = True 46 | 47 | def load_cache(self): 48 | if os.path.isfile(self.cache_file): 49 | with open(self.cache_file, "r") as f: 50 | self.cache = json.load(f) 51 | 52 | def save_cache(self): 53 | with open(self.cache_file, "w") as f: 54 | json.dump(self.cache, f) 55 | 56 | def get_sample_key(self, document, generated): 57 | return "%s|%%%%|%%|%s" % (document, generated) 58 | 59 | def score_questeval(self, documents, generateds, **kwargs): 60 | scores = [] 61 | for document, generated in zip(documents, generateds): 62 | score = self.questeval.compute_all(document, generated) 63 | scores.append(score["scores"]["fscore"]) 64 | return {"scores": scores} 65 | 66 | def score_feqa(self, documents, generateds, **kwargs): 67 | scores = self.scorer.compute_score(documents, generateds, aggregate=False) 68 | self.save_cache() 69 | return {"scores": scores} 70 | 71 | def score_dae(self, documents, generateds, **kwargs): 72 | from evaluate_factuality import score_example_single_context 73 | 74 | scores = [] 75 | for document, generated in zip(documents, generateds): 76 | document = " ".join(document.split(" ")[:250]) 77 | score = score_example_single_context(generated, document, self.dae_model, self.tokenizer, self.args).item() 78 | scores.append(score) 79 | 80 | # self.save_cache() 81 | return {"scores": scores} 82 | 83 | def score(self, documents, generateds, **kwargs): 84 | new_samples = [] 85 | for d, g in zip(documents, generateds): 86 | k = self.get_sample_key(d, g) 87 | if k not in self.cache: 88 | new_samples.append((k, d, g)) 89 | 90 | if len(new_samples) > 0: 91 | if not self.model_loaded: 92 | self.load_model() 93 | 94 | if self.model == "questeval": 95 | new_scores = self.score_questeval([d[1] for d in new_samples], [d[2] for d in new_samples]) 96 | elif self.model == "feqa": 97 | new_scores = self.score_feqa([d[1] for d in new_samples], [d[2] for d in new_samples]) 98 | elif self.model == "dae": 99 | new_scores = self.score_dae([d[1] for d in new_samples], [d[2] for d in new_samples]) 100 | 101 | for (k, d, g), score in zip(new_samples, new_scores["scores"]): 102 | self.cache[k] = score 103 | 104 | return {"scores": [self.cache[self.get_sample_key(d, g)] for d, g in zip(documents, generateds)]} 105 | 106 | 107 | if __name__ == "__main__": 108 | hypothesis = """After wildfires consumed an entire town, students and teachers who had planned for remote classes found some comfort in staying connected amid the chaos.""" 109 | 110 | source = """Ash fell from an apocalyptic orange sky as Jennifer Willin drove home last week from the only school in tiny Berry Creek, Calif., where she had picked up a pair of Wi-Fi hot spots for her daughters’ remote classes. Hours later, 111 | her cellphone erupted with an emergency alert: Evacuate immediately. By the next morning, what one official described as a “massive wall of fire” had swept through the entire Northern California town of about 1,200 people, killing nine residents, 112 | including a 16-year-old boy, and destroying the school and almost every home and business. Ms. Willin and her family escaped to a cramped hotel room 60 miles away. 113 | In her panic, she had forgotten to grab masks, but she had the hot spots, along with her daughters’ laptops and school books. On Monday, the two girls plan to meet with their teachers on Zoom, seeking some comfort amid the chaos. 114 | They’re still able to be in school,” Ms. Willin said, “even though the school burned to the ground.” 115 | As the worst wildfire season in decades scorches the West amid a still raging pandemic, families and educators who were already starting the strangest and most challenging school year of their lifetimes have been traumatized all over again. 116 | Tens of thousands of people have been forced to flee their homes, with some mourning the loss of their entire communities. 117 | But amid the twin disasters, the remote learning preparations that schools made for the coronavirus are providing a strange 118 | modicum of stability for teachers and students, letting many stay connected and take comfort in an unexpected form of virtual community.""" 119 | 120 | qe_score = BaselineScorer(model="dae") 121 | print(qe_score.score([source], [hypothesis])) 122 | -------------------------------------------------------------------------------- /summac/model_guardrails.py: -------------------------------------------------------------------------------- 1 | import nltk, spacy 2 | 3 | def doc2ents(doc, black_list_types=[]): 4 | ents = [{"type": ent.label_, "entity": ent.text, "sent_idx": sent_idx} for sent_idx, sent in enumerate(doc.sents) for ent in sent.ents] 5 | ents = [e for e in ents if e["type"] not in black_list_types] 6 | return ents 7 | 8 | 9 | class NERInaccuracyPenalty: 10 | def __init__(self, spacy_model="en_core_web_sm"): 11 | 12 | common_ents = ["one", "united states", "army"] 13 | self.common_ents = set([cent.lower() for cent in common_ents]) 14 | self.spacy_model = spacy.load(spacy_model) 15 | self.word2num = {} 16 | self.black_list_types = set(["ORDINAL", "WORK_OF_ART", "EVENT","PRODUCT", "LAW", "LANGUAGE"]) 17 | self.number_words_to_remove = set(["the", "a", "an", "at", "of", "in", "than", "several", "few", "only", "about", "another", "least", "most", "last", "first", "early", "earlier", "later", "over", "fewer", "row", "every", "late", "ago", "only", "about", "around", "within", "more", "less"]) 18 | 19 | self.string2digits = {"zero": 0, "one": 1, "two": 2, "three": 3, "four": 4, "five": 5, "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10, "eleven": 11, "twelve": 12, "thirteen": 13, "fourteen": 14, "fifteen": 15, "sixteen": 16, "seventeen": 17, "eighteen": 18, "nineteen": 19, "twenty": 20, "thirty": 30, "forty": 40, "fifty": 50, "sixty": 60, "seventy": 70, "eighty": 80, "ninety": 90, "a hundred": 100, "hundred": 100, "a thousand": 1000, "thousand": 1000} 20 | self.string2digits = {k: str(v) for k, v in self.string2digits.items()} 21 | self.digits2string = {v:k for k,v in self.string2digits.items()} 22 | 23 | def common_ents_no_problem(self, ent_text): 24 | return ent_text in self.common_ents 25 | 26 | def clean_entity_text(self, ent_text): 27 | ent_text = ent_text.lower().replace("-", " ").replace('"', '').strip().replace("'s", "") 28 | if ent_text[:4] == "the ": 29 | ent_text = ent_text[4:] 30 | return ent_text.strip() 31 | 32 | def singular(self, ent_text): 33 | if len(ent_text) == 0: 34 | return ent_text 35 | if ent_text[-1] == "s": 36 | return ent_text[:-1] 37 | else: 38 | return ent_text 39 | 40 | def quantifier_cleaning(self, quantifier_text): 41 | words = nltk.tokenize.word_tokenize(quantifier_text.lower()) 42 | words = sorted([w for w in words if len(w) >= 2 and w not in self.number_words_to_remove]) 43 | return set(words) 44 | 45 | def quantifier_matching(self, quantifier, entity_list): 46 | quantifier_clean = self.quantifier_cleaning(quantifier) 47 | entity_list_clean = [self.quantifier_cleaning(ent["text"]) for ent in entity_list] 48 | return any([quantifier_clean in ent2_clean for ent2_clean in entity_list_clean]) 49 | 50 | def remove_common_entities(self, ent_list_new, ent_list_old, source_text): 51 | source_text = source_text.lower() 52 | 53 | ent_set = set([self.clean_entity_text(e["text"]) for e in ent_list_old]) 54 | finals = [] 55 | 56 | for ent_new in ent_list_new: 57 | raw_entity_lower = ent_new["text"].lower() 58 | entity_text = self.clean_entity_text(ent_new["text"]) 59 | if self.common_ents_no_problem(entity_text): # The entity is too common and could added anywhere 60 | continue 61 | if entity_text in ent_set or self.singular(entity_text) in ent_set: # Exact match with some entity 62 | continue 63 | if entity_text in source_text or self.singular(entity_text).lower() in source_text or raw_entity_lower in source_text: # Sometimes the NER model won't tag the exact same thing in the original paragraph, but we can just do string matching 64 | continue 65 | # Starting the entity-specific matching 66 | if ent_new["type"] in ["DATE", "CARDINAL", "MONEY", "PERCENT"]: 67 | # For dates: 68 | # a subset match is allowed: "several months" -> "months", "only a few weeks" -> "a few weeks" 69 | quantifier_clean = self.quantifier_cleaning(ent_new["text"]) 70 | if self.quantifier_matching(ent_new["text"], ent_list_old): 71 | # if any([clean_string in ent_text2 for ent_text2 in ent_set]): 72 | continue 73 | 74 | if all([w in source_text for w in quantifier_clean]): 75 | # A bit more desperate: remove additional words, and check that what's left is in the original 76 | continue 77 | if ent_new["type"] == "CARDINAL": 78 | if raw_entity_lower in self.string2digits and self.string2digits[raw_entity_lower] in source_text: 79 | continue # They wrote "nineteen" instead of 19 80 | elif raw_entity_lower in self.digits2string and self.digits2string[raw_entity_lower] in source_text.replace(",", ""): 81 | continue # They wrote 19 instead of "nineteen" 82 | 83 | if ent_new["type"] == "GPE": 84 | if entity_text+"n" in ent_set or entity_text[:-1] in ent_set: 85 | # If you say india instead of indian, or indian instead of india. 86 | # Definitely doesn't work with every country, could use a lookup table 87 | continue 88 | if ent_new["type"] in ["ORG", "PERSON"]: 89 | # Saying a smaller thing is fine: Barack Obama -> Obama. University of California, Berkeley -> University of California 90 | if any([entity_text in ent_text2 for ent_text2 in ent_set]): 91 | continue 92 | finals.append(ent_new) 93 | return finals 94 | 95 | def score_one(self, ents1, ents2, source): 96 | new_ents2 = self.remove_common_entities(ents2, ents1, source) 97 | score = 1.0 if len(new_ents2) > 0 else 0.0 98 | return {"score": score, "new_ents": new_ents2, "gen_entities": ents2, "source_entities": ents1} 99 | 100 | def extract_entities(self, text): 101 | doc = self.spacy_model(text) 102 | return [{"text": ent.text, "type": ent.label_} for ent in doc.ents] 103 | 104 | def score(self, sources, generateds, printing=False, **kwargs): 105 | source_ents = [self.extract_entities(text) for text in sources] 106 | generated_ents = [self.extract_entities(text) for text in generateds] 107 | 108 | scores, all_new_ents = [], [] 109 | for source_ent, generated_ent, source in zip(source_ents, generated_ents, sources): 110 | out = self.score_one(source_ent, generated_ent, source) 111 | scores.append(out["score"]) 112 | all_new_ents.append(out["new_ents"]) 113 | # gen_ents.append(out["gen_entities"]) 114 | # source_ents.append(out["source_entities"]) 115 | # if printing: 116 | # print("NER Inaccuracy:", out["new_ents"]) 117 | return {"scores": scores, "source_ents": source_ents, "gen_ents": generated_ents, "new_ents": all_new_ents} 118 | 119 | 120 | if __name__ == "__main__": 121 | start = "Increases the amount of such credit to 50 percent for contributions to schools or public libraries in empowerment zones, enterprise communities, and Indian reservations." 122 | end = "Increases the blabla of such credit to 50 percent." 123 | 124 | ner_hallu = NERInaccuracyPenalty() 125 | 126 | print(ner_hallu.score([start], [end])) 127 | -------------------------------------------------------------------------------- /summac/model_summac.py: -------------------------------------------------------------------------------- 1 | from transformers import AutoTokenizer, AutoModelForSequenceClassification 2 | import nltk, numpy as np, torch, os, json 3 | from .utils_misc import batcher 4 | 5 | model_map = { 6 | "snli-base": {"model_card": "boychaboy/SNLI_roberta-base", "entailment_idx": 0, "contradiction_idx": 2}, 7 | "snli-large": {"model_card": "boychaboy/SNLI_roberta-large", "entailment_idx": 0, "contradiction_idx": 2}, 8 | "mnli-base": {"model_card": "microsoft/deberta-base-mnli", "entailment_idx": 2, "contradiction_idx": 0}, 9 | "mnli": {"model_card": "roberta-large-mnli", "entailment_idx": 2, "contradiction_idx": 0}, 10 | "anli": {"model_card": "ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli", "entailment_idx": 0, "contradiction_idx": 2}, 11 | "vitc-base": {"model_card": "tals/albert-base-vitaminc-mnli", "entailment_idx": 0, "contradiction_idx": 1}, 12 | "vitc": {"model_card": "tals/albert-xlarge-vitaminc-mnli", "entailment_idx": 0, "contradiction_idx": 1}, 13 | "vitc-only": {"model_card": "tals/albert-xlarge-vitaminc", "entailment_idx": 0, "contradiction_idx": 1}, 14 | # "decomp": 0, 15 | } 16 | 17 | 18 | def card_to_name(card): 19 | card2name = {v["model_card"]: k for k, v in model_map.items()} 20 | if card in card2name: 21 | return card2name[card] 22 | return card 23 | 24 | 25 | def name_to_card(name): 26 | if name in model_map: 27 | return model_map[name]["model_card"] 28 | return name 29 | 30 | 31 | def get_neutral_idx(ent_idx, con_idx): 32 | return list(set([0, 1, 2]) - set([ent_idx, con_idx]))[0] 33 | 34 | 35 | class SummaCImager: 36 | def __init__(self, model_name="mnli", granularity="paragraph", use_cache=True, max_doc_sents=100, device="cuda", **kwargs): 37 | 38 | self.grans = granularity.split("-") 39 | 40 | assert all(gran in ["paragraph", "sentence", "document", "2sents", "mixed"] for gran in self.grans) and len(self.grans) <= 2, "Unrecognized `granularity` %s" % (granularity) 41 | assert model_name in model_map.keys(), "Unrecognized model name: `%s`" % (model_name) 42 | 43 | self.model_name = model_name 44 | if model_name != "decomp": 45 | self.model_card = name_to_card(model_name) 46 | self.entailment_idx = model_map[model_name]["entailment_idx"] 47 | self.contradiction_idx = model_map[model_name]["contradiction_idx"] 48 | self.neutral_idx = get_neutral_idx(self.entailment_idx, self.contradiction_idx) 49 | 50 | self.granularity = granularity 51 | self.use_cache = use_cache 52 | self.cache_folder = "/export/share/plaban/summac_cache/" 53 | 54 | self.max_doc_sents = max_doc_sents 55 | self.max_input_length = 500 56 | self.device = device 57 | self.cache = {} 58 | self.model = None # Lazy loader 59 | 60 | def load_nli(self): 61 | self.tokenizer = AutoTokenizer.from_pretrained(self.model_card) 62 | self.model = AutoModelForSequenceClassification.from_pretrained(self.model_card).eval() 63 | self.model.to(self.device) 64 | if self.device == "cuda": 65 | self.model.half() 66 | 67 | def split_sentences(self, text): 68 | sentences = nltk.tokenize.sent_tokenize(text) 69 | sentences = [sent for sent in sentences if len(sent)>10] 70 | return sentences 71 | 72 | def split_2sents(self, text): 73 | sentences = nltk.tokenize.sent_tokenize(text) 74 | sentences = [sent for sent in sentences if len(sent)>10] 75 | two_sents = [" ".join(sentences[i:(i+2)]) for i in range(len(sentences))] 76 | return two_sents 77 | 78 | def split_paragraphs(self, text): 79 | if text.count("\n\n") > 0: 80 | paragraphs = [p.strip() for p in text.split("\n\n")] 81 | else: 82 | paragraphs = [p.strip() for p in text.split("\n")] 83 | return [p for p in paragraphs if len(p) > 10] 84 | 85 | def split_text(self, text, granularity="sentence"): 86 | if granularity == "document": 87 | return [text] 88 | elif granularity == "paragraph": 89 | return self.split_paragraphs(text) 90 | elif granularity == "sentence": 91 | return self.split_sentences(text) 92 | elif granularity == "2sents": 93 | return self.split_2sents(text) 94 | elif granularity == "mixed": 95 | return self.split_sentences(text) + self.split_paragraphs(text) 96 | 97 | def build_chunk_dataset(self, original, generated, pair_idx=None): 98 | if len(self.grans) == 1: 99 | gran_doc, gran_sum = self.grans[0], self.grans[0] 100 | else: 101 | gran_doc, gran_sum = self.grans[0], self.grans[1] 102 | 103 | original_chunks = self.split_text(original, granularity=gran_doc)[:self.max_doc_sents] 104 | generated_chunks = self.split_text(generated, granularity=gran_sum) 105 | 106 | N_ori, N_gen = len(original_chunks), len(generated_chunks) 107 | dataset = [{"premise": original_chunks[i], "hypothesis": generated_chunks[j], "doc_i": i, "gen_i": j, "pair_idx": pair_idx} for i in range(N_ori) for j in range(N_gen)] 108 | return dataset, N_ori, N_gen 109 | 110 | def build_image(self, original, generated): 111 | cache_key = (original, generated) 112 | if self.use_cache and cache_key in self.cache: 113 | cached_image = self.cache[cache_key] 114 | cached_image = cached_image[:, :self.max_doc_sents, :] 115 | return cached_image 116 | 117 | dataset, N_ori, N_gen = self.build_chunk_dataset(original, generated) 118 | 119 | if len(dataset) == 0: 120 | return np.zeros((3, 1, 1)) 121 | 122 | image = np.zeros((3, N_ori, N_gen)) 123 | 124 | if self.model is None: 125 | self.load_nli() 126 | 127 | for batch in batcher(dataset, batch_size=20): 128 | batch_prems = [b["premise"] for b in batch] 129 | batch_hypos = [b["hypothesis"] for b in batch] 130 | batch_tokens = self.tokenizer.batch_encode_plus(list(zip(batch_prems, batch_hypos)), padding=True, truncation=True, max_length=self.max_input_length, return_tensors="pt", truncation_strategy="only_first") 131 | with torch.no_grad(): 132 | model_outputs = self.model(**{k: v.to(self.device) for k, v in batch_tokens.items()}) 133 | 134 | batch_probs = torch.nn.functional.softmax(model_outputs["logits"], dim=-1) 135 | batch_evids = batch_probs[:, self.entailment_idx].tolist() 136 | batch_conts = batch_probs[:, self.contradiction_idx].tolist() 137 | batch_neuts = batch_probs[:, self.neutral_idx].tolist() 138 | 139 | for b, evid, cont, neut in zip(batch, batch_evids, batch_conts, batch_neuts): 140 | image[0, b["doc_i"], b["gen_i"]] = evid 141 | image[1, b["doc_i"], b["gen_i"]] = cont 142 | image[2, b["doc_i"], b["gen_i"]] = neut 143 | 144 | if self.use_cache: 145 | self.cache[cache_key] = image 146 | return image 147 | 148 | def build_images(self, originals, generateds, batch_size=128): 149 | todo_originals, todo_generateds = [], [] 150 | for ori, gen in zip(originals, generateds): 151 | cache_key = (ori, gen) 152 | if cache_key not in self.cache: 153 | todo_originals.append(ori) 154 | todo_generateds.append(gen) 155 | 156 | total_dataset = [] 157 | todo_images = [] 158 | for pair_idx, (ori, gen) in enumerate(zip(todo_originals, todo_generateds)): 159 | dataset, N_ori, N_gen = self.build_chunk_dataset(ori, gen, pair_idx=pair_idx) 160 | if len(dataset) == 0: 161 | image = np.zeros((3, 1, 1)) 162 | else: 163 | image = np.zeros((3, N_ori, N_gen)) 164 | todo_images.append(image) 165 | total_dataset += dataset 166 | if len(total_dataset) > 0 and self.model is None: # Can't just rely on the cache 167 | self.load_nli() 168 | 169 | for batch in batcher(total_dataset, batch_size=batch_size): 170 | batch_prems = [b["premise"] for b in batch] 171 | batch_hypos = [b["hypothesis"] for b in batch] 172 | batch_tokens = self.tokenizer.batch_encode_plus(list(zip(batch_prems, batch_hypos)), padding=True, truncation=True, max_length=self.max_input_length, return_tensors="pt", truncation_strategy="only_first") 173 | with torch.no_grad(): 174 | model_outputs = self.model(**{k: v.to(self.device) for k, v in batch_tokens.items()}) 175 | 176 | batch_probs = torch.nn.functional.softmax(model_outputs["logits"], dim=-1) 177 | batch_evids = batch_probs[:, self.entailment_idx].tolist() 178 | batch_conts = batch_probs[:, self.contradiction_idx].tolist() 179 | batch_neuts = batch_probs[:, self.neutral_idx].tolist() 180 | 181 | for b, evid, cont, neut in zip(batch, batch_evids, batch_conts, batch_neuts): 182 | image = todo_images[b["pair_idx"]] 183 | image[0, b["doc_i"], b["gen_i"]] = evid 184 | image[1, b["doc_i"], b["gen_i"]] = cont 185 | image[2, b["doc_i"], b["gen_i"]] = neut 186 | 187 | for pair_idx, (ori, gen) in enumerate(zip(todo_originals, todo_generateds)): 188 | cache_key = (ori, gen) 189 | self.cache[cache_key] = todo_images[pair_idx] 190 | 191 | images = [self.cache[(ori, gen)] for ori, gen in zip(originals, generateds)] 192 | return images 193 | 194 | def get_cache_file(self): 195 | return os.path.join(self.cache_folder, "cache_%s_%s.json" % (self.model_name, self.granularity)) 196 | 197 | def save_cache(self): 198 | cache_cp = {"[///]".join(k): v.tolist() for k, v in self.cache.items()} 199 | with open(self.get_cache_file(), "w") as f: 200 | json.dump(cache_cp, f) 201 | 202 | def load_cache(self): 203 | cache_file = self.get_cache_file() 204 | if os.path.isfile(cache_file): 205 | with open(cache_file, "r") as f: 206 | cache_cp = json.load(f) 207 | self.cache = {tuple(k.split("[///]")): np.array(v) for k, v in cache_cp.items()} 208 | 209 | class SummaCConv(torch.nn.Module): 210 | def __init__(self, models=["mnli", "anli", "vitc"], bins='even50', granularity="sentence", nli_labels="e", device="cuda", start_file=None, imager_load_cache=True, agg="mean", **kwargs): 211 | # `bins` should be `even%d` or `percentiles` 212 | assert nli_labels in ["e", "c", "n", "ec", "en", "cn", "ecn"], "Unrecognized nli_labels argument %s" % (nli_labels) 213 | 214 | super(SummaCConv, self).__init__() 215 | self.device = device 216 | self.models = models 217 | 218 | self.imagers = [] 219 | for model_name in models: 220 | self.imagers.append(SummaCImager(model_name=model_name, granularity=granularity, device=self.device, **kwargs)) 221 | if imager_load_cache: 222 | for imager in self.imagers: 223 | imager.load_cache() 224 | assert len(self.imagers)>0, "Imager names were empty or unrecognized" 225 | 226 | if "even" in bins: 227 | n_bins = int(bins.replace("even", "")) 228 | self.bins = list(np.arange(0, 1, 1/n_bins)) + [1.0] 229 | elif bins == "percentile": 230 | self.bins = [0.0, 0.01, 0.02, 0.03, 0.04, 0.07, 0.13, 0.37, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.955, 0.96, 0.965, 0.97, 0.975, 0.98, 0.985, 0.99, 0.995, 1.0] # Based on the percentile of the distribution on some large number of summaries 231 | 232 | self.nli_labels = nli_labels 233 | self.n_bins = len(self.bins) - 1 234 | self.n_rows = 10 235 | self.n_labels = 2 236 | self.n_depth = len(self.imagers)*len(self.nli_labels) 237 | self.full_size = self.n_depth*self.n_bins 238 | 239 | self.agg = agg 240 | 241 | self.mlp = torch.nn.Linear(self.full_size, 1).to(device) 242 | self.layer_final = torch.nn.Linear(3, self.n_labels).to(device) 243 | 244 | if start_file == "default": 245 | start_file = "summac_conv_vitc_sent_perc_e.bin" 246 | if not os.path.isfile("summac_conv_vitc_sent_perc_e.bin"): 247 | os.system("wget https://github.com/tingofurro/summac/raw/master/summac_conv_vitc_sent_perc_e.bin") 248 | assert bins == "percentile", "bins mode should be set to percentile if using the default 1-d convolution weights." 249 | if start_file is not None: 250 | print(self.load_state_dict(torch.load(start_file))) 251 | 252 | def build_image(self, original, generated): 253 | images = [imager.build_image(original, generated) for imager in self.imagers] 254 | image = np.concatenate(images, axis=0) 255 | return image 256 | 257 | def compute_histogram(self, original=None, generated=None, image=None): 258 | # Takes the two texts, and generates a (n_rows, 2*n_bins) 259 | 260 | if image is None: 261 | image = self.build_image(original, generated) 262 | 263 | N_depth, N_ori, N_gen = image.shape 264 | 265 | full_histogram = [] 266 | for i_gen in range(N_gen): 267 | histos = [] 268 | 269 | for i_depth in range(N_depth): 270 | if (i_depth % 3 == 0 and "e" in self.nli_labels) or (i_depth % 3 == 1 and "c" in self.nli_labels) or (i_depth % 3 == 2 and "n" in self.nli_labels): 271 | histo, X = np.histogram(image[i_depth, :, i_gen], range=(0, 1), bins=self.bins, density=False) 272 | histos.append(histo) 273 | 274 | histogram_row = np.concatenate(histos) 275 | full_histogram.append(histogram_row) 276 | 277 | n_rows_missing = self.n_rows - len(full_histogram) 278 | full_histogram += [[0.0] * self.full_size] * n_rows_missing 279 | full_histogram = full_histogram[:self.n_rows] 280 | full_histogram = np.array(full_histogram) 281 | return image, full_histogram 282 | 283 | def forward(self, originals, generateds, images=None): 284 | if images is not None: 285 | # In case they've been pre-computed. 286 | histograms = [] 287 | for image in images: 288 | _, histogram = self.compute_histogram(image=image) 289 | histograms.append(histogram) 290 | else: 291 | images, histograms = [], [] 292 | for original, generated in zip(originals, generateds): 293 | image, histogram = self.compute_histogram(original=original, generated=generated) 294 | images.append(image) 295 | histograms.append(histogram) 296 | 297 | N = len(histograms) 298 | histograms = torch.FloatTensor(histograms).to(self.device) 299 | 300 | non_zeros = (torch.sum(histograms, dim=-1) != 0.0).long() 301 | seq_lengths = non_zeros.sum(dim=-1).tolist() 302 | 303 | mlp_outs = self.mlp(histograms).reshape(N, self.n_rows) 304 | features = [] 305 | 306 | for mlp_out, seq_length in zip(mlp_outs, seq_lengths): 307 | if seq_length > 0: 308 | Rs = mlp_out[:seq_length] 309 | if self.agg == "mean": 310 | features.append(torch.cat([torch.mean(Rs).unsqueeze(0), torch.mean(Rs).unsqueeze(0), torch.mean(Rs).unsqueeze(0)]).unsqueeze(0)) 311 | elif self.agg == "min": 312 | features.append(torch.cat([torch.min(Rs).unsqueeze(0), torch.min(Rs).unsqueeze(0), torch.min(Rs).unsqueeze(0)]).unsqueeze(0)) 313 | elif self.agg == "max": 314 | features.append(torch.cat([torch.max(Rs).unsqueeze(0), torch.max(Rs).unsqueeze(0), torch.max(Rs).unsqueeze(0)]).unsqueeze(0)) 315 | elif self.agg == "all": 316 | features.append(torch.cat([torch.min(Rs).unsqueeze(0), torch.mean(Rs).unsqueeze(0), torch.max(Rs).unsqueeze(0)]).unsqueeze(0)) 317 | else: 318 | features.append(torch.FloatTensor([0.0, 0.0, 0.0]).unsqueeze(0)) # .cuda() 319 | features = torch.cat(features) 320 | logits = self.layer_final(features) 321 | histograms_out = [histogram.cpu().numpy() for histogram in histograms] 322 | return logits, histograms_out, images 323 | 324 | def save_imager_cache(self): 325 | for imager in self.imagers: 326 | imager.save_cache() 327 | 328 | def score(self, originals, generateds, **kwargs): 329 | with torch.no_grad(): 330 | logits, histograms, images = self.forward(originals, generateds) 331 | probs = torch.nn.functional.softmax(logits, dim=-1) 332 | batch_scores = probs[:, 1].tolist() 333 | return {"scores": batch_scores} # , "histograms": histograms, "images": images 334 | 335 | 336 | class SummaCZS: 337 | def __init__(self, model_name="mnli", granularity="paragraph", op1="max", op2="mean", use_ent=True, use_con=True, imager_load_cache=True, device="cuda", **kwargs): 338 | assert op2 in ["min", "mean", "max"], "Unrecognized `op2`" 339 | assert op1 in ["max", "mean", "min"], "Unrecognized `op1`" 340 | self.device = device 341 | self.imager = SummaCImager(model_name=model_name, granularity=granularity, device=self.device, **kwargs) 342 | if imager_load_cache: 343 | self.imager.load_cache() 344 | self.op2 = op2 345 | self.op1 = op1 346 | self.use_ent = use_ent 347 | self.use_con = use_con 348 | 349 | def save_imager_cache(self): 350 | self.imager.save_cache() 351 | 352 | def score_one(self, original, generated): 353 | image = self.imager.build_image(original, generated) 354 | score = self.image2score(image) 355 | return {"image": image, "score": score} 356 | 357 | def image2score(self, image): 358 | ent_scores = np.max(image[0], axis=0) 359 | co_scores = np.max(image[1], axis=0) 360 | if self.op1 == "mean": 361 | ent_scores = np.mean(image[0], axis=0) 362 | co_scores = np.mean(image[1], axis=0) 363 | elif self.op1 == "min": 364 | ent_scores = np.min(image[0], axis=0) 365 | co_scores = np.min(image[1], axis=0) 366 | 367 | if self.use_ent and self.use_con: 368 | scores = ent_scores - co_scores 369 | elif self.use_ent: 370 | scores = ent_scores 371 | elif self.use_con: 372 | scores = 1.0 - co_scores 373 | 374 | final_score = np.mean(scores) 375 | if self.op2 == "min": 376 | final_score = np.min(scores) 377 | elif self.op2 == "max": 378 | final_score = np.max(scores) 379 | return final_score 380 | 381 | def score(self, sources, generateds, batch_size=128, **kwargs): 382 | images = self.imager.build_images(sources, generateds, batch_size=batch_size) 383 | scores = [self.image2score(image) for image in images] 384 | return {"scores": scores, "images": images} 385 | 386 | 387 | if __name__ == "__main__": 388 | model = SummaCZS(granularity="document", model_name="vitc", imager_load_cache=True, device="cpu") # Device can be `cpu` or `cuda` when GPU is available 389 | 390 | document = "Jeff joined Microsoft in 1992 to lead corporate developer evangelism for Windows NT." 391 | summary1 = "Jeff joined Microsoft in 1992." 392 | summary2 = "Jeff joined Microsoft." 393 | 394 | print(model.score([document, document], [summary1, summary2])["scores"]) 395 | 396 | # document = """Jeff joined Microsoft in 1992 to lead corporate developer evangelism for Windows NT. He then served as a Group Program manager in Microsoft's Internet Business Unit. In 1998, he led the creation of SharePoint Portal Server, which became one of Microsoft’s fastest-growing businesses, exceeding $2 billion in revenues. Jeff next served as Corporate Vice President for Program Management across Office 365 Services and Servers, which is the foundation of Microsoft's enterprise cloud leadership. He then led Corporate Strategy supporting Satya Nadella and Amy Hood on Microsoft's mobile-first/cloud-first transformation and acquisitions. Prior to joining Microsoft, Jeff was vice president for software development for an investment firm in New York. He leads Office shared experiences and core applications, as well as OneDrive and SharePoint consumer and business services in Office 365. Jeff holds a Master of Business Administration degree from Harvard Business School and a Bachelor of Science degree in information systems and finance from New York University.""" 397 | # summary = "Jeff joined Microsoft in 1992 to lead the company's corporate evangelism. He then served as a Group Manager in Microsoft's Internet Business Unit. In 1998, Jeff led Sharepoint Portal Server, which became the company's fastest-growing business, surpassing $3 million in revenue. Jeff next leads corporate strategy for SharePoint and Servers which is the basis of Microsoft's cloud-first strategy. He leads corporate strategy for Satya Nadella and Amy Hood on Microsoft's mobile-first." 398 | 399 | # scores = model.score([document], [summary])["images"][0][0].T 400 | # summary_sentences = model.imager.split_text(summary) 401 | 402 | # print(np.array2string(scores, precision=2)) 403 | # for score_row, sentence in zip(scores, summary_sentences): 404 | # print("-----------") 405 | # print("[SummaC score: %.3f; supporting sentence: %d] %s " % (np.max(score_row), np.argmax(score_row)+1, sentence)) 406 | -------------------------------------------------------------------------------- /summac/run_baseline.py: -------------------------------------------------------------------------------- 1 | # This code does not work as is, but was kept for reference, if others are interested in re-running baselines. 2 | from utils_summac_benchmark import load_factcc, load_polytope, load_cogensumm, load_frank, load_summeval, load_xsumfaith 3 | import sklearn, numpy as np, os, pandas as pd, sys, argparse 4 | from model_baseline import BaselineScorer 5 | from utils_scoring import ScorerWrapper 6 | import utils_misc, seaborn as sns 7 | from collections import Counter 8 | 9 | sys.path.insert(0, "/home/phillab/feqa/") 10 | os.environ["CUDA_VISIBLE_DEVICES"] = "0" 11 | 12 | # For now, can't use argparse because it is hard-coded in DAE... very shitty 13 | # parser = argparse.ArgumentParser() 14 | # parser.add_argument("--model", type=str, choices=["questeval", "feqa", "dae"], default="questeval") 15 | # parser.add_argument("--questeval_weighter", action="store_true") 16 | # parser.add_argument("--cut", type=str, choices=["val", "test"], default="val") 17 | # args = parser.parse_args() 18 | 19 | model = "dae" 20 | cut = "test" 21 | 22 | utils_misc.DoublePrint("%s_%s.log" % (model, cut)) 23 | 24 | def choose_best_threshold(labels, scores): 25 | best_bacc = 0.0 26 | best_thresh = 0.0 27 | thresholds = [np.percentile(scores, p) for p in np.arange(0, 100, 1)] 28 | for thresh in thresholds: 29 | preds = [1 if score > thresh else 0 for score in scores] 30 | bacc_score = sklearn.metrics.balanced_accuracy_score(labels, preds) 31 | if bacc_score >= best_bacc: 32 | best_bacc = bacc_score 33 | best_thresh = thresh 34 | return best_thresh 35 | 36 | def from_score_to_pred(dataset, score_key): 37 | scores = [d[score_key] for d in dataset] 38 | labels = [d["label"] for d in dataset] 39 | thresh = choose_best_threshold(labels, scores) 40 | 41 | pred_key = "pred_%s" % (score_key) 42 | for d in dataset: 43 | d[pred_key] = 1 if d[score_key]>thresh else 0 44 | 45 | 46 | datasets = [{"name": "factcc", "dataset": load_factcc(cut=cut)}, 47 | {"name": "frank", "dataset": load_frank(cut=cut)}, 48 | {"name": "pt_any", "dataset": load_polytope(which_label="overall", cut=cut)}, 49 | {"name": "summ_corr", "dataset": load_cogensumm(cut=cut)}, 50 | {"name": "summeval", "dataset": load_summeval(cut=cut)}, 51 | {"name": "xsumfaith", "dataset": load_xsumfaith(cut=cut)} 52 | ] 53 | 54 | dataset_stats = [] 55 | for dataset in datasets: 56 | N_pos, N_neg = len([d for d in dataset["dataset"] if d["label"]==1]), len([d for d in dataset["dataset"] if d["label"]==0]) 57 | dataset_stats.append({"name": dataset["name"], "N": len(dataset["dataset"]), "N_pos": N_pos, "N_neg": N_neg, "frac_pos": N_pos/(N_pos+N_neg)}) 58 | 59 | print(pd.DataFrame(dataset_stats)) 60 | 61 | scorers = [] 62 | if model == "questeval": 63 | scorers.append({"name": "QuestEval", "model": BaselineScorer(model="questeval", do_weighter=args.questeval_weighter), "sign": 1}) 64 | elif model == "feqa": 65 | scorers.append({"name": "FEQA", "model": BaselineScorer(model="feqa"), "sign": 1}) 66 | elif model == "dae": 67 | scorers.append({"name": "DAE", "model": BaselineScorer(model="dae"), "sign": 1}) 68 | 69 | 70 | for scorer in scorers: 71 | scorer["model"].load_cache() 72 | 73 | batch_size = 100 74 | scorer_doc = ScorerWrapper(scorers, scoring_method="sum", max_batch_size=batch_size, use_caching=True) 75 | 76 | def compute_doc_level(dataset): 77 | documents = [d["document"] for d in dataset] 78 | summaries = [d["claim"] for d in dataset] 79 | doc_scores = scorer_doc(documents, summaries, progress=True) 80 | label_keys = [k for k in doc_scores.keys() if "_scores" in k] 81 | 82 | for label_key in label_keys: 83 | score_key = ("%s|doc" % (label_key)).replace("_scores", "") 84 | for d, score in zip(dataset, doc_scores[label_key]): 85 | d[score_key] = score 86 | from_score_to_pred(dataset, score_key) 87 | 88 | 89 | results = [] 90 | for dataset in datasets: 91 | print("======= %s ========" % (dataset["name"])) 92 | datas = dataset["dataset"] 93 | compute_doc_level(datas) 94 | for scorer in scorers: 95 | scorer["model"].save_cache() 96 | 97 | pred_labels = [k for k in datas[0].keys() if "pred_" in k] 98 | for pred_label in pred_labels: 99 | preds = [d[pred_label] for d in datas] 100 | labels = [d["label"] for d in datas] 101 | model_name, input_type = pred_label.replace("pred_", "").split("|") 102 | 103 | label_counts = Counter([d["label"] for d in dataset["dataset"]]) 104 | pos_label = 0 if label_counts[0] < label_counts[1] else 1 105 | 106 | f1 = sklearn.metrics.f1_score(labels, preds, pos_label=pos_label) 107 | balanced_acc = sklearn.metrics.balanced_accuracy_score(labels, preds) 108 | 109 | results.append({"model_name": model_name, "dataset_name": dataset["name"], "input": input_type, "%s_f1" % (dataset["name"]): f1, "%s_bacc" % (dataset["name"]): balanced_acc}) 110 | 111 | cm = sns.light_palette("green", as_cmap=True) 112 | 113 | def highlight_max(data): 114 | is_max = data == data.max() 115 | return ['font-weight: bold' if v else '' for v in is_max] 116 | 117 | 118 | df = pd.DataFrame(results) 119 | df = df.groupby(["model_name", "input"]).agg({"%s_bacc" % (d["name"]): "mean" for d in datasets}) 120 | 121 | df = df.rename(columns={k: k.replace("_bacc", "") for k in df.keys()}) 122 | df.drop("total", inplace=True) 123 | 124 | print(df) 125 | df.to_csv("/home/phillab/%s_results.csv" % (model)) 126 | # print(df.style.apply(highlight_max).background_gradient(cmap=cm, high=1.0, low=0.0).set_precision(3).set_caption("Weighed Accuracy")) 127 | -------------------------------------------------------------------------------- /summac/train_summac.py: -------------------------------------------------------------------------------- 1 | from .utils_misc import select_freer_gpu 2 | 3 | select_freer_gpu() 4 | import torch, tqdm, nltk, numpy as np, argparse, json 5 | from torch.utils.data import DataLoader, RandomSampler 6 | from .utils_optim import build_optimizer 7 | from .benchmark import SummaCBenchmark, load_factcc 8 | from .model_summac import SummaCConv, model_map 9 | import os, time 10 | 11 | def train(model="mnli", granularity="sentence", nli_labels="e", pre_file="", num_epochs=5, optimizer="adam", train_batch_size=32, learning_rate=0.1, bins="even50", silent=False, norm_histo=False): 12 | experiment = "%s_%s_%s_%s" % (model, granularity, bins, nli_labels) 13 | 14 | if not silent: 15 | print("Experiment name: %s" % (experiment)) 16 | 17 | if len(pre_file) == 0: 18 | standard_pre_file = "/home/phillab/data/summac_cache/train_%s_%s.jsonl" % (model, granularity) 19 | if os.path.isfile(standard_pre_file): 20 | pre_file = standard_pre_file 21 | 22 | precomputed = len(pre_file) > 0 23 | device = "cpu" if precomputed else "cuda" 24 | 25 | if model == "multi": 26 | models = ["mnli", "anli", "vitc"] 27 | elif model == "multi2": 28 | models = ["mnli", "vitc", "vitc-only", "vitc-base"] 29 | else: 30 | models = [model] 31 | 32 | model = SummaCConv(models=models, granularity=granularity, nli_labels=nli_labels, device=device, bins=bins, norm_histo=norm_histo) 33 | 34 | optimizer = build_optimizer(model, learning_rate=learning_rate, optimizer_name=optimizer) 35 | if not silent: 36 | print("Model Loaded") 37 | 38 | def sent_tok(text): 39 | sentences = nltk.tokenize.sent_tokenize(text) 40 | return [sent for sent in sentences if len(sent)>10] 41 | 42 | def collate_func(inps): 43 | documents, claims, labels = [], [], [] 44 | for inp in inps: 45 | if len(sent_tok(inp["claim"])) > 0 and len(sent_tok(inp["document"])) > 0: 46 | documents.append(inp["document"]) 47 | claims.append(inp["claim"]) 48 | labels.append(inp["label"]) 49 | labels = torch.LongTensor(labels).to(device) 50 | return documents, claims, labels 51 | 52 | def collate_pre(inps): 53 | documents = [inp["document"] for inp in inps] 54 | claims = [inp["claim"] for inp in inps] 55 | # images = [[np.array(im) for im in inp["image"]] for inp in inps] 56 | images = [np.array(inp["image"]) for inp in inps] 57 | labels = torch.LongTensor([inp["label"] for inp in inps]).to(device) 58 | return documents, claims, images, labels 59 | 60 | if precomputed: 61 | d_train = [] 62 | with open(pre_file, "r") as f: 63 | for line in f: 64 | d_train.append(json.loads(line)) 65 | dl_train = DataLoader(dataset=d_train, batch_size=train_batch_size, sampler=RandomSampler(d_train), collate_fn=collate_pre) 66 | else: 67 | d_train = load_factcc(cut="train") 68 | dl_train = DataLoader(dataset=d_train, batch_size=train_batch_size, sampler=RandomSampler(d_train), collate_fn=collate_func) 69 | 70 | fcb = SummaCBenchmark(cut="val") 71 | 72 | if not silent: 73 | print("Length of dataset. [Training: %d]" % (len(d_train))) 74 | 75 | crit = torch.nn.CrossEntropyLoss() 76 | eval_every = 200 77 | best_val_score = 0.0 78 | best_file = "" 79 | 80 | for epi in range(num_epochs): 81 | ite = enumerate(dl_train) 82 | if not silent: 83 | ite = tqdm.tqdm(ite, total=len(dl_train)) 84 | for ib, batch in ite: 85 | if precomputed: 86 | documents, claims, images, batch_labels = batch 87 | logits, _, _ = model(documents, claims, images=images) 88 | else: 89 | documents, claims, batch_labels = batch 90 | logits, _, _ = model(originals=documents, generateds=claims) 91 | loss = crit(logits, batch_labels) 92 | 93 | loss.backward() 94 | optimizer.step() 95 | optimizer.zero_grad() 96 | # wandb.log({"loss": loss.item()}) 97 | 98 | if ib % eval_every == eval_every-1: 99 | 100 | eval_time = time.time() 101 | benchmark = fcb.evaluate(model) 102 | val_score = benchmark["overall_score"] 103 | eval_time = time.time() - eval_time 104 | if eval_time > 10.0: 105 | model.save_imager_cache() 106 | 107 | if not silent: 108 | ite.set_description("[Benchmark Score: %.3f]" % (val_score)) 109 | if val_score > best_val_score: 110 | best_val_score = val_score 111 | if len(best_file) > 0: 112 | os.remove(best_file) 113 | best_file = "/home/phillab/models/summac/%s_bacc%.3f.bin" % (experiment, best_val_score) 114 | torch.save(model.state_dict(), best_file) 115 | if not silent: 116 | for t in benchmark["benchmark"]: 117 | print("[%s] Score: %.3f (thresh: %.3f)" % (t["name"].ljust(10), t["score"], t["threshold"])) 118 | return best_val_score 119 | 120 | 121 | if __name__ == "__main__": 122 | parser = argparse.ArgumentParser() 123 | 124 | model_choices = list(model_map.keys()) + ["multi", "multi2"] 125 | 126 | parser.add_argument("--model", type=str, choices=model_choices, default="mnli") 127 | parser.add_argument("--granularity", type=str, default="sentence") # , choices=["sentence", "paragraph", "mixed", "2sents"] 128 | parser.add_argument("--pre_file", type=str, default="", help="If not empty, will use the precomputed instead of computing images on the fly. (useful for hyper-param tuning)") 129 | parser.add_argument("--bins", type=str, default="percentile", help="How should the bins of the histograms be decided (even%d or percentile)") 130 | parser.add_argument("--nli_labels", type=str, default="e", choices=["e", "c", "n", "ec", "en", "cn", "ecn"], help="Which of the three labels should be used in the creation of the histogram") 131 | 132 | parser.add_argument("--num_epochs", type=int, default=5, help="Number of passes over the data.") 133 | parser.add_argument("--optimizer", type=str, choices=["adam", "sgd"], default="adam") 134 | parser.add_argument("--train_batch_size", type=int, default=32, help="Training batch size.") 135 | parser.add_argument("--learning_rate", type=float, default=1e-2, help="Number of passes over the data.") 136 | parser.add_argument("--norm_histo", action="store_true", help="Normalize the histogram to be between 0 and 1, and include the explicit count") 137 | 138 | args = parser.parse_args() 139 | train(**args.__dict__) 140 | -------------------------------------------------------------------------------- /summac/utils_misc.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import requests 3 | import tqdm 4 | import os 5 | 6 | # GPU-related business 7 | 8 | def get_freer_gpu(): 9 | os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp_smi') 10 | memory_available = [int(x.split()[2])+5*i for i, x in enumerate(open('tmp_smi', 'r').readlines())] 11 | os.remove("tmp_smi") 12 | return np.argmax(memory_available) 13 | 14 | def select_freer_gpu(): 15 | freer_gpu = str(get_freer_gpu()) 16 | print("Will use GPU: %s" % (freer_gpu)) 17 | os.environ['CUDA_LAUNCH_BLOCKING'] = "1" 18 | os.environ["CUDA_VISIBLE_DEVICES"] = ""+freer_gpu 19 | return freer_gpu 20 | 21 | def batcher(iterator, batch_size=4, progress=False): 22 | if progress: 23 | iterator = tqdm.tqdm(iterator) 24 | 25 | batch = [] 26 | for elem in iterator: 27 | batch.append(elem) 28 | if len(batch) == batch_size: 29 | final_batch = batch 30 | batch = [] 31 | yield final_batch 32 | if len(batch) > 0: # Leftovers 33 | yield batch 34 | 35 | # Google Drive related 36 | 37 | def download_file_from_google_drive(id, destination): 38 | URL = "https://docs.google.com/uc?export=download" 39 | 40 | session = requests.Session() 41 | 42 | response = session.get(URL, params = { 'id' : id }, stream = True) 43 | token = get_confirm_token(response) 44 | 45 | if token: 46 | params = { 'id' : id, 'confirm' : token } 47 | response = session.get(URL, params = params, stream = True) 48 | 49 | save_response_content(response, destination) 50 | 51 | def get_confirm_token(response): 52 | for key, value in response.cookies.items(): 53 | if key.startswith('download_warning'): 54 | return value 55 | 56 | return None 57 | 58 | def save_response_content(response, destination): 59 | CHUNK_SIZE = 32768 60 | 61 | with open(destination, "wb") as f: 62 | for chunk in response.iter_content(CHUNK_SIZE): 63 | if chunk: # filter out keep-alive new chunks 64 | f.write(chunk) 65 | -------------------------------------------------------------------------------- /summac/utils_optim.py: -------------------------------------------------------------------------------- 1 | from transformers.optimization import AdamW 2 | from torch.optim.lr_scheduler import LambdaLR 3 | from torch.optim import Optimizer, SGD 4 | from torch import nn 5 | import math, torch 6 | 7 | def build_optimizer(model, optimizer_name="adam", learning_rate=1e-5): 8 | param_optimizer = list(model.named_parameters()) 9 | no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] 10 | optimizer_grouped_parameters = [ 11 | {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, 12 | {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} 13 | ] 14 | if optimizer_name == "adam": 15 | optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate) 16 | elif optimizer_name == "sgd": 17 | optimizer = SGD(optimizer_grouped_parameters, lr=learning_rate) 18 | else: 19 | assert False, "optimizer_name = '%s' is not `adam` or `lamb`" % (optimizer_name) 20 | return optimizer 21 | -------------------------------------------------------------------------------- /summac/utils_scorer.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import sklearn 3 | 4 | # Choosing threshold 5 | def choose_best_threshold(labels, scores): 6 | best_f1 = 0.0 7 | best_thresh = 0.0 8 | thresholds = [np.percentile(scores, p) for p in np.arange(0, 100, 0.2)] 9 | for thresh in thresholds: 10 | preds = [1 if score > thresh else 0 for score in scores] 11 | f1_score = sklearn.metrics.balanced_accuracy_score(labels, preds) 12 | 13 | if f1_score >= best_f1: 14 | best_f1 = f1_score 15 | best_thresh = thresh 16 | return best_thresh, best_f1 17 | 18 | def from_score_to_pred(dataset, score_key): 19 | scores = [d[score_key] for d in dataset] 20 | labels = [d["label"] for d in dataset] 21 | thresh, _ = choose_best_threshold(labels, scores) 22 | 23 | pred_key = "pred_%s" % (score_key) 24 | for d in dataset: 25 | d[pred_key] = 1 if d[score_key] > thresh else 0 26 | 27 | 28 | # Score computation utility 29 | def compute_doc_level(scorer_doc, dataset): 30 | documents = [d["document"] for d in dataset] 31 | summaries = [d["claim"] for d in dataset] 32 | doc_scores = scorer_doc(documents, summaries, progress=True) 33 | label_keys = [k for k in doc_scores.keys() if "_scores" in k] 34 | 35 | for label_key in label_keys: 36 | score_key = ("%s|doc" % (label_key)).replace("_scores", "") 37 | for d, score in zip(dataset, doc_scores[label_key]): 38 | d[score_key] = score 39 | from_score_to_pred(dataset, score_key) 40 | 41 | def compute_paragraph_level(scorer_para, dataset): 42 | all_paragraphs = [] 43 | all_summaries = [] 44 | idx_map = [] 45 | for i, d in enumerate(dataset): 46 | separator = "\n\n" if d["document"].count("\n\n")>0 else "\n" 47 | paragraphs = d["document"].split(separator) 48 | paragraphs = [p.strip() for p in paragraphs if len(p.strip()) > 0] 49 | all_paragraphs += paragraphs 50 | all_summaries += [d["claim"]] * len(paragraphs) 51 | idx_map += [i] * len(paragraphs) 52 | 53 | para_scores = scorer_para(all_paragraphs, all_summaries, progress=True) 54 | label_keys = [sname+"_scores" for sname in scorer_para.get_score_names()] 55 | for label_key in label_keys: 56 | score_key = ("%s|paras" % (label_key)).replace("_scores", "") 57 | for d in dataset: 58 | d[score_key] = [] 59 | for j, score in enumerate(para_scores[label_key]): 60 | dataset[idx_map[j]][score_key].append(score) 61 | 62 | mean_k, max_k, min_k = score_key+"_mean", score_key+"_max", score_key+"_min" 63 | for d in dataset: 64 | d[mean_k] = np.mean(d[score_key]) 65 | d[max_k] = np.max(d[score_key]) 66 | d[min_k] = np.min(d[score_key]) 67 | from_score_to_pred(dataset, mean_k) 68 | from_score_to_pred(dataset, max_k) 69 | from_score_to_pred(dataset, min_k) 70 | -------------------------------------------------------------------------------- /summac/utils_scoring.py: -------------------------------------------------------------------------------- 1 | import torch, time, numpy as np 2 | from .utils_misc import batcher 3 | 4 | class ScorerWrapper: 5 | def __init__(self, scorers, scoring_method="logsum", max_batch_size=100, use_caching=False): 6 | assert scoring_method in ["sum", "product", "logsum"], "Unrecognized `scoring_method`" 7 | 8 | self.scorers = scorers 9 | self.scoring_method = scoring_method 10 | self.use_caching = use_caching 11 | self.cache = {} 12 | 13 | self.max_batch_size = max_batch_size 14 | if self.scoring_method == "logsum": 15 | self.score_func = logsum_score 16 | elif self.scoring_method == "product": 17 | self.score_func = product_score 18 | elif self.scoring_method == "sum": 19 | self.score_func = sum_score 20 | 21 | def get_score_names(self): 22 | return [s["name"] for s in self.scorers] 23 | 24 | def make_key(self, inp, gen): 25 | return "%s|||___|||%s" % (inp, gen) 26 | 27 | def score(self, inputs, generateds, partial=False, printing=False, timings=False, extras={}, progress=False): 28 | assert len(inputs) == len(generateds), "Input and output lengths don't match" 29 | 30 | if not self.use_caching: 31 | self.cache = {} # Reset the cache 32 | 33 | todo = [] 34 | all_keys = [] 35 | for inp, gen in zip(inputs, generateds): 36 | key = self.make_key(inp, gen) 37 | all_keys.append(key) 38 | if key not in self.cache: 39 | todo.append({"inp": inp, "gen": gen, "key": key}) 40 | 41 | for d in todo: 42 | self.cache[d["key"]] = {} 43 | 44 | if self.use_caching and len(todo) < len(all_keys): 45 | print("With caching, only processing: %d / %d samples" % (len(todo), len(all_keys))) 46 | 47 | if len(todo) == 0: 48 | progress = False # Not needed, it's empty 49 | 50 | for batch_todo in batcher(todo, batch_size=self.max_batch_size, progress=progress): 51 | batch_inputs = [d["inp"] for d in batch_todo] 52 | batch_gens = [d["gen"] for d in batch_todo] 53 | 54 | batch_scores, timings_out = self.score_func(self.scorers, batch_inputs, batch_gens, partial=partial, printing=printing, extras=extras) 55 | 56 | for k, out in batch_scores.items(): 57 | if type(out) in [torch.Tensor, np.array, np.ndarray]: 58 | out = out.tolist() 59 | 60 | for i, d in enumerate(batch_todo): 61 | self.cache[d["key"]][k] = out[i] 62 | 63 | if timings: 64 | print(timings_out) 65 | 66 | all_outputs = {} 67 | for k in self.cache[all_keys[0]].keys(): 68 | all_outputs[k] = [self.cache[key][k] for key in all_keys] 69 | 70 | if printing: 71 | print("[total]", all_outputs["total_scores"]) 72 | return all_outputs 73 | 74 | def __call__(self, inputs, generateds, **kwargs): 75 | return self.score(inputs, generateds, **kwargs) 76 | 77 | def sum_score(scorers, paragraphs, generateds, partial=False, printing=False, extras={}): 78 | total_scores = np.zeros((len(paragraphs))) 79 | scorer_returns, timings = {}, {} 80 | T = time.time() 81 | 82 | for scorer in scorers: 83 | scores = scorer['model'].score(paragraphs, generateds, partial=partial, printing=printing, **extras) 84 | weight = scorer.get("weight", 1.0) 85 | total_scores += scorer["sign"]*weight*np.array(scores['scores']) 86 | 87 | scorer_returns.update({scorer['name']+"_"+k: v for k, v in scores.items()}) 88 | timings[scorer["name"]] = time.time()-T 89 | T = time.time() 90 | 91 | scorer_returns['total_scores'] = total_scores 92 | return scorer_returns, timings 93 | 94 | def product_score(scorers, paragraphs, generateds, partial=False, printing=False, extras={}): 95 | total_scores = np.ones((len(paragraphs))) 96 | scorer_returns, timings = {}, {} 97 | T = time.time() 98 | 99 | for scorer in scorers: 100 | scores = scorer['model'].score(paragraphs, generateds, partial=partial, printing=printing, **extras) 101 | if scorer['sign'] == 1: 102 | total_scores *= np.array(scores['scores']) 103 | else: # It's a binary penalty 104 | total_scores *= (1-np.array(scores['scores'])) 105 | 106 | scorer_returns.update({scorer['name']+"_"+k: v for k, v in scores.items()}) 107 | timings[scorer["name"]] = time.time()-T 108 | T = time.time() 109 | 110 | scorer_returns['total_scores'] = total_scores 111 | return scorer_returns, timings 112 | 113 | def logsum_score(scorers, paragraphs, generateds, partial=False, printing=False, extras={}): 114 | total_scores = np.zeros((len(paragraphs))) 115 | scorer_returns, timings = {}, {} 116 | T = time.time() 117 | 118 | for scorer in scorers: 119 | scores = scorer['model'].score(paragraphs, generateds, partial=partial, printing=printing, **extras) 120 | weight = scorer.get("weight", 1.0) 121 | scores["scores"] = np.clip(scores["scores"], 0.0001, 0.9999) 122 | if scorer['sign'] == 1: 123 | total_scores += weight*np.log(np.array(scores['scores'])) 124 | else: # It's a binary penalty 125 | total_scores += np.log(1-np.array(scores["scores"])) 126 | 127 | scorer_returns.update({scorer['name']+"_"+k: v for k, v in scores.items()}) 128 | timings[scorer["name"]] = time.time()-T 129 | T = time.time() 130 | 131 | scorer_returns['total_scores'] = total_scores 132 | return scorer_returns, timings 133 | -------------------------------------------------------------------------------- /summac_conv_vitc_sent_perc_e.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tingofurro/summac/c1f3da93cd074c24d8033eb27a88b5a7cc5c08fa/summac_conv_vitc_sent_perc_e.bin --------------------------------------------------------------------------------