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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 | ## Code repository for "ClimateBERT: A Pretrained Language Model for Climate-Related Text" 2 | 3 | Link to paper: [arxiv.org/abs/2110.12010](https://arxiv.org/abs/2110.12010) 4 | 5 | ## Usage 6 | The usage is straightforward and comprises two steps: 7 | 8 | 1. The tokenizer is augmented with potentially new tokens which represent climate change specific language. This step led to the inclusion of tokens such as 'CO2' or 'CH4' which are often key to properly representing text. The code for this step can be found in 'tokenizer_augmentation.ipynb'. Besides [transformer package](https://huggingface.co/), this step also requires the [transformers_domain_adaptation](https://github.com/georgian-io/Transformers-Domain-Adaptation) packages. 9 | 2. Using the augmented tokenizer, the next step is to train the language model. This step follows basic steps from [transformer package](https://huggingface.co/). We provide the code for this in 'language_modeling.ipynb'. 10 | 11 | ## Dependencies 12 | Our code depends on the [transformer package](https://huggingface.co/) and on [transformers_domain_adaptation](https://github.com/georgian-io/Transformers-Domain-Adaptation). For training ClimateBert, we used transformer 4.20. and transformers_domain_adaptation 0.3.1. 13 | 14 | ## How do I cite ClimateBert? 15 | 16 | For now, cite the [Arxiv paper](https://arxiv.org/abs/2110.12010): 17 | 18 | ``` 19 | @article{webersinke2021climatebert, 20 | title={Climatebert: A pretrained language model for climate-related text}, 21 | author={Webersinke, Nicolas and Kraus, Mathias and Bingler, Julia Anna and Leippold, Markus}, 22 | journal={arXiv preprint arXiv:2110.12010}, 23 | year={2021} 24 | } 25 | ``` 26 | -------------------------------------------------------------------------------- /tokenizer_augmentation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## CLIMATEBERT: A Pretrained Language Model for Climate-Related Text\n", 8 | "#### by Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, and Markus Leippold\n", 9 | "#### Link to paper: [arxiv.org/abs/2110.12010](https://arxiv.org/abs/2110.12010)\n", 10 | "#### Code Part 1: Tokenizer augmentation" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "Note: Run this notebook in a new virtual environment since \"transformers_domain_adaptation\" depends on an old version of \"transformers\" that you most likely don't want in your training environment, let alone globally.\n", 18 | "\n", 19 | "Import libraries" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 1, 25 | "metadata": { 26 | "colab": { 27 | "base_uri": "https://localhost:8080/" 28 | }, 29 | "id": "idic6OckjyUD", 30 | "outputId": "6b98bd95-d526-4516-c84e-c282e847b276" 31 | }, 32 | "outputs": [], 33 | "source": [ 34 | "from transformers import AutoTokenizer\n", 35 | "from transformers import Trainer, TrainingArguments\n", 36 | "from transformers import AutoModelForMaskedLM\n", 37 | "from transformers import DataCollatorForLanguageModeling\n", 38 | "from sklearn.model_selection import train_test_split\n", 39 | "from datasets import load_dataset\n", 40 | "\n", 41 | "import random\n", 42 | "import math\n", 43 | "from transformers_domain_adaptation import DataSelector, VocabAugmentor" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": {}, 49 | "source": [ 50 | "Read corpus and perform train-test split" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 4, 56 | "metadata": { 57 | "id": "ZDYDn3s-iPOt" 58 | }, 59 | "outputs": [], 60 | "source": [ 61 | "with open('./corpus.txt', 'r') as file:\n", 62 | " corpus = [line.rstrip(\"\\n\") for line in file.readlines()] \n", 63 | "\n", 64 | "train_text, val_text = train_test_split(corpus, test_size=0.15, random_state=42)\n", 65 | "\n", 66 | "with open('corpus/train_corpus.txt', 'w') as file:\n", 67 | " for paragraph in train_text:\n", 68 | " file.write(paragraph + \"\\n\")\n", 69 | " \n", 70 | "with open('corpus/val_corpus.txt', 'w') as file:\n", 71 | " for paragraph in val_text:\n", 72 | " file.write(paragraph + \"\\n\")" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "Load model of choice" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": null, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [ 88 | "card = \"distilroberta-base\"\n", 89 | "\n", 90 | "tokenizer = AutoTokenizer.from_pretrained(card, use_fast=True) \n", 91 | "model = AutoModelForMaskedLM.from_pretrained(card)" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "metadata": {}, 97 | "source": [ 98 | "Read the texts of the final downstream task at hand" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 6, 104 | "metadata": {}, 105 | "outputs": [], 106 | "source": [ 107 | "with open('./fine_tuning_texts.txt', 'r') as file:\n", 108 | " fine_tuning_texts = [line.rstrip(\"\\n\") for line in file.readlines()]" 109 | ] 110 | }, 111 | { 112 | "cell_type": "markdown", 113 | "metadata": {}, 114 | "source": [ 115 | "Perform data selection" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": null, 121 | "metadata": {}, 122 | "outputs": [], 123 | "source": [ 124 | "# Batch data due to memory limits\n", 125 | "\n", 126 | "batch_size_fine_tuning = 200\n", 127 | "batch_size_corpus = 100000\n", 128 | "train_text_copy = train_text.copy()\n", 129 | "random.shuffle(train_text_copy)\n", 130 | "\n", 131 | "selected_corpus = []\n", 132 | "j = 1\n", 133 | "\n", 134 | "for i in range(0, len(train_text_copy), batch_size_corpus):\n", 135 | " print(f\"Processing batch {j} of {math.ceil(len(train_text_copy) / batch_size_corpus)}...\")\n", 136 | " j += 1\n", 137 | " train_text_batch = train_text_copy[i:i + batch_size_corpus]\n", 138 | " \n", 139 | " selector = DataSelector(\n", 140 | " keep=0.7,\n", 141 | " tokenizer=tokenizer,\n", 142 | " #similarity_metrics=['euclidean'],\n", 143 | " diversity_metrics=[\n", 144 | " \"type_token_ratio\",\n", 145 | " \"entropy\",\n", 146 | " ],\n", 147 | " )\n", 148 | " \n", 149 | " selector.fit(random.sample(fine_tuning_texts, batch_size_fine_tuning))\n", 150 | "\n", 151 | " selected_corpus.extend(selector.transform(train_text_batch))" 152 | ] 153 | }, 154 | { 155 | "cell_type": "markdown", 156 | "metadata": {}, 157 | "source": [ 158 | "Write selected train corpus" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 7, 164 | "metadata": {}, 165 | "outputs": [], 166 | "source": [ 167 | "with open('corpus/train_selected_corpus.txt', 'w') as file:\n", 168 | " for paragraph in selected_corpus:\n", 169 | " file.write(paragraph + \"\\n\")" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": null, 175 | "metadata": {}, 176 | "outputs": [], 177 | "source": [ 178 | "print(\"Current vocab size: \" + str(len(tokenizer)))" 179 | ] 180 | }, 181 | { 182 | "cell_type": "markdown", 183 | "metadata": {}, 184 | "source": [ 185 | "Augment the tokenizer" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": null, 191 | "metadata": {}, 192 | "outputs": [], 193 | "source": [ 194 | "new_vocab_size = 50_500 # Adjust to needs\n", 195 | "\n", 196 | "augmentor = VocabAugmentor(\n", 197 | " tokenizer=tokenizer, \n", 198 | " cased=True, # Depends on model\n", 199 | " target_vocab_size=new_vocab_size\n", 200 | ")\n", 201 | "\n", 202 | "new_tokens = augmentor.get_new_tokens('./fine_tuning_texts.txt')\n", 203 | "\n", 204 | "tokenizer.add_tokens(new_tokens)\n", 205 | "\n", 206 | "model.resize_token_embeddings(len(tokenizer))\n", 207 | "\n", 208 | "print(\"New vocab size: \" + str(len(tokenizer)))\n", 209 | "\n", 210 | "tokenizer.save_pretrained(\"model/distilroberta-base-augmented\")\n", 211 | "model.save_pretrained(\"model/distilroberta-base-augmented\")" 212 | ] 213 | } 214 | ], 215 | "metadata": { 216 | "accelerator": "GPU", 217 | "colab": { 218 | "collapsed_sections": 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