├── En_Hi_language_translation.ipynb ├── Fine-Tuning-BERT-for-NER.ipynb ├── README.md └── Text-Summarization-Fine-tuning-Transformers-model.ipynb /Fine-Tuning-BERT-for-NER.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOeKsUmuzBoJxKd7auSPZs+"},"kernelspec":{"name":"python3","display_name":"Python 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classification\n","\n","\n","The first application we’ll explore is token classification. This generic task encompasses any problem that can be formulated as “attributing a label to each token in a sentence,” such as:\n","\n","**Named entity recognition (NER)**: Find the entities (such as persons, locations, or organizations) in a sentence. This can be formulated as attributing a label to each token by having one class per entity and one class for “no entity.”\n","\n","**Part-of-speech tagging (POS)**: Mark each word in a sentence as corresponding to a particular part of speech (such as noun, verb, adjective, etc.).\n","\n","**Chunking**: Find the tokens that belong to the same entity. This task (which can be combined with POS or NER) can be formulated as attributing one label (usually B-) to any tokens that are at the beginning of a chunk, another label (usually I-) to tokens that are inside a chunk, and a third label (usually O) to tokens that don’t belong to any chunk.\n","\n","\n","\n","- O means the word doesn’t correspond to any entity.\n","- B-PER/I-PER means the word corresponds to the beginning of/is inside a person entity.\n","- B-ORG/I-ORG means the word corresponds to the beginning of/is inside an organization entity.\n","- B-LOC/I-LOC means the word corresponds to the beginning of/is inside a location entity.\n","- B-MISC/I-MISC means the word corresponds to the beginning of/is inside a miscellaneous entity."],"metadata":{"id":"tTRM5xsH7ejC"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"M3_wQXox7Tng","executionInfo":{"status":"ok","timestamp":1669466283156,"user_tz":-360,"elapsed":17102,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"8cda968b-56cc-4bf4-94c6-a84f4631ae2a"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[K |████████████████████████████████| 5.5 MB 32.7 MB/s \n","\u001b[K |████████████████████████████████| 451 kB 61.7 MB/s \n","\u001b[K |████████████████████████████████| 7.6 MB 50.9 MB/s \n","\u001b[K |████████████████████████████████| 43 kB 2.0 MB/s \n","\u001b[K |████████████████████████████████| 182 kB 66.9 MB/s \n","\u001b[K |████████████████████████████████| 212 kB 71.9 MB/s \n","\u001b[K |████████████████████████████████| 115 kB 69.1 MB/s \n","\u001b[K |████████████████████████████████| 127 kB 70.0 MB/s \n","\u001b[?25h Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n"]}],"source":["# Install\n","!pip install transformers datasets tokenizers seqeval -q"]},{"cell_type":"markdown","source":["Data link: https://huggingface.co/datasets/conll2003"],"metadata":{"id":"qWzD1XRD8nAQ"}},{"cell_type":"code","source":["import datasets \n","import numpy as np \n","from transformers import BertTokenizerFast \n","from transformers import DataCollatorForTokenClassification \n","from transformers import AutoModelForTokenClassification \n","\n","conll2003 = datasets.load_dataset(\"conll2003\") "],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["0fc860da84c044409702d67c11f4dc3b","02708236262a42e9b591e372579ad6f5","622724afd86a453b93ab34ca366c3f47","9453a9a4f48b48f188790e1e1149986a","9d70b790768a49378b0ce581e278713f","188599ebef7642309950b6bcb9bc8f9b","fda8ecb441774291b6d3f9d29661858d","6b94ce718a214d8e8e72ee2b652e26ac","30df6e056c054ac598069fbddf534caa","b202e65fc7c1409dbc19dad507c85444","efbd05e0215745009873d93684f4c37a","d9076686ec584fecbd0ceb3a14c78282","4c1dc67027ef4aab805f1b9bfcf1b443","2be9d961e61643db8850966b5824887f","01c3127b468c410284d0445a6fcd8f17","95e92a598b5e42948985a6f94fe0ec91","d9c84ea4abc94a5b890c8d0bd7d5677b","ac4feb1492184461a640daa94383f5a9","f8fa45d187da48da865300bec2b41a37","9e17a4950f0448209051b7c8baa279bb","1ab943ef8db244b7956ec64410d583ad","7f579c91d12d4fa494b576829dba8e1a","f7902440c7a94362926e5b01be7f0886","ec73938897a942f19329a87a4aeb950c","c059ee94cf0b45b8bd0b57af534915b6","ee78e8d5e6f5496ea1553b6c42145d0f","73050862503d49beaec5236b2462346e","ac0968f1a6c1449aba0be704b5948119","140640ccf91448c3821eee22be0ce55b","5ce1ad535d54445f85c1cdda72d4247a","16d68137fdd14f1da19ad00e97707aca","279da01d53724a2782da867439277690","cf5bdb836b164ec5919bc15f114ff645","7fb267efe43b41e98add5f4a7aae5beb","ab7d21c46c444f70882c301554439a81","27c479e3c09547289ade3654192cdd54","9b71c22618c3490a9a4b8e86bfb28724","b50dec761b3b4765a07c4a6ed8b8154f","756c7c735eb54f11998bef010fce27ee","d495f38aae024ca6bc3f169b0c4a127c","53ed584f320043bdace55012acd4255e","18255993a28c4ba09335d70bf4cf834e","38a290379f2d42e2b8ae2e3fda5632e1","969f385a375f440c8d780b9fb37ca460","2b0b3171962a4f9395391618fe1f1b9e","20bfa0118216419db6599377fb909053","c0f6f71bca254da883265df6c7a725d1","106454e8f51744b59688837b776ab2bf","3e453b4c4b684ba1b231a3e0e88848d6","2f55246e2d014e8d8ed60ce0be9882c4","f5a585889292460ba3a0226712583264","068e09325188476898cf25dd6504fee5","0140e61717a3406dbcc9bd286de8b6c8","a746a911ea8d4c0a89c204d148529815","8981000eea2141dcabd0769286724d87","8b35f247efae4e26b6e9bb2ca18e0a63","f30e9d9bb13243f297c3bf74d5e303f9","8349fb02a90a47a78a24346f1fd06030","e2452a2994a6454688dcc0de59f2e847","c8a1a85907624967a91923e53011d644","52c1990b66304e329e8ab00ae2c1e923","4a9258f395e14dc68b3069fe96dc9d2c","f34e591923aa4e1ba9040f961a4de418","546e4e04bbd34f7bb626ab56faec9f9a","ec62b458234d497fab7beb41022ea4b6","2f3910e96eba42d8be634b870b10f044","c4c3ce57f48e4caaa5ae3e9a7f5a87a2","28573c2211fa4f6fb05d4741a30fdb38","5e2a5eb708d2444b8b2633b452c820e6","9dbfb801fd1148e0aa4e5296abb115d5","475ae998cf7d4f4ab3d0b6a362b7aed8","ed3824b616eb4739bebba8868eba4bad","1d615f45085e48c18cb93392dbf797c2","0498be9125dc4150933c79aacdb85bdb","d6bf43f0602b40609c98683f46792c11","552e3ec679ed4e6db1b94a9c45e47810","ea0d0cc917394f02b3db148f1e89064a","77b7cf8273c1433c8a283c0208a0f551","93a57928c15c43dcb1063d8d774ed8ce","2db4345997b448a9b0c14bb55a733cb7","5ddaf82e706b4849bb86dcaf760b51ac","e6356434f9b44115a2dc1410c71b47de","95947cb9940049948e92de7102b896af","a36fb26454864a848fa6b9648d4d9235","f380a1176eb34bcb829f399f826d6965","ebfee3fb97cb4bddadd012d5f1372daf","a16a84f05c49443fa66fcd34e615cacf","283576fd477344aea8165f8c44c6eb40"]},"id":"kwtbLhqR8fUo","executionInfo":{"status":"ok","timestamp":1669466336466,"user_tz":-360,"elapsed":30615,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"00f25c78-c75a-4c6c-8f2f-fb8e772f4d03"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/9.57k [00:00 Return a list mapping the tokens\n"," # to their actual word in the initial sentence.\n"," # It Returns a list indicating the word corresponding to each token. \n"," previous_word_idx = None \n"," label_ids = []\n"," # Special tokens like `` and `<\\s>` are originally mapped to None \n"," # We need to set the label to -100 so they are automatically ignored in the loss function.\n"," for word_idx in word_ids: \n"," if word_idx is None: \n"," # set –100 as the label for these special tokens\n"," label_ids.append(-100)\n"," # For the other tokens in a word, we set the label to either the current label or -100, depending on\n"," # the label_all_tokens flag.\n"," elif word_idx != previous_word_idx:\n"," # if current word_idx is != prev then its the most regular case\n"," # and add the corresponding token \n"," label_ids.append(label[word_idx]) \n"," else: \n"," # to take care of sub-words which have the same word_idx\n"," # set -100 as well for them, but only if label_all_tokens == False\n"," label_ids.append(label[word_idx] if label_all_tokens else -100) \n"," # mask the subword representations after the first subword\n"," \n"," previous_word_idx = word_idx \n"," labels.append(label_ids) \n"," tokenized_inputs[\"labels\"] = labels \n"," return tokenized_inputs "],"metadata":{"id":"ZoOQqR3A97Zp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["conll2003['train'][4:5]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Vrepo0W_DbZ5","executionInfo":{"status":"ok","timestamp":1669468166408,"user_tz":-360,"elapsed":3,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"08f1c9b8-8f04-408d-ea81-a086251d3f8a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'id': ['4'],\n"," 'tokens': [['Germany',\n"," \"'s\",\n"," 'representative',\n"," 'to',\n"," 'the',\n"," 'European',\n"," 'Union',\n"," \"'s\",\n"," 'veterinary',\n"," 'committee',\n"," 'Werner',\n"," 'Zwingmann',\n"," 'said',\n"," 'on',\n"," 'Wednesday',\n"," 'consumers',\n"," 'should',\n"," 'buy',\n"," 'sheepmeat',\n"," 'from',\n"," 'countries',\n"," 'other',\n"," 'than',\n"," 'Britain',\n"," 'until',\n"," 'the',\n"," 'scientific',\n"," 'advice',\n"," 'was',\n"," 'clearer',\n"," '.']],\n"," 'pos_tags': [[22,\n"," 27,\n"," 21,\n"," 35,\n"," 12,\n"," 22,\n"," 22,\n"," 27,\n"," 16,\n"," 21,\n"," 22,\n"," 22,\n"," 38,\n"," 15,\n"," 22,\n"," 24,\n"," 20,\n"," 37,\n"," 21,\n"," 15,\n"," 24,\n"," 16,\n"," 15,\n"," 22,\n"," 15,\n"," 12,\n"," 16,\n"," 21,\n"," 38,\n"," 17,\n"," 7]],\n"," 'chunk_tags': [[11,\n"," 11,\n"," 12,\n"," 13,\n"," 11,\n"," 12,\n"," 12,\n"," 11,\n"," 12,\n"," 12,\n"," 12,\n"," 12,\n"," 21,\n"," 13,\n"," 11,\n"," 12,\n"," 21,\n"," 22,\n"," 11,\n"," 13,\n"," 11,\n"," 1,\n"," 13,\n"," 11,\n"," 17,\n"," 11,\n"," 12,\n"," 12,\n"," 21,\n"," 1,\n"," 0]],\n"," 'ner_tags': [[5,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 3,\n"," 4,\n"," 0,\n"," 0,\n"," 0,\n"," 1,\n"," 2,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 5,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0]]}"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["q = tokenize_and_align_labels(conll2003['train'][4:5]) \n","print(q) "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QWiK4JvUA2a5","executionInfo":{"status":"ok","timestamp":1669468176720,"user_tz":-360,"elapsed":753,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"9ff8863e-7475-46b3-d3de-093dcd6930bc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': [[101, 2762, 1005, 1055, 4387, 2000, 1996, 2647, 2586, 1005, 1055, 15651, 2837, 14121, 1062, 9328, 5804, 2056, 2006, 9317, 10390, 2323, 4965, 8351, 4168, 4017, 2013, 3032, 2060, 2084, 3725, 2127, 1996, 4045, 6040, 2001, 24509, 1012, 102]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'labels': [[-100, 5, 0, 0, 0, 0, 0, 3, 4, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, -100]]}\n"]}]},{"cell_type":"markdown","source":["So before applying the tokenize_and_align_labels() the tokenized_input has 3 keys\n","- input_ids\n","- token_type_ids\n","- attention_mask\n","\n","But after applying tokenize_and_align_labels() we have an extra key - 'labels'"],"metadata":{"id":"2pv_1-u2Ek8q"}},{"cell_type":"code","source":["for token, label in zip(tokenizer.convert_ids_to_tokens(q[\"input_ids\"][0]),q[\"labels\"][0]): \n"," print(f\"{token:_<40} {label}\") "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sxrkYNhpEoPD","executionInfo":{"status":"ok","timestamp":1669468430493,"user_tz":-360,"elapsed":599,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"7d51b4dd-597e-41cc-8ba1-403ca8e3aed9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[CLS]___________________________________ -100\n","germany_________________________________ 5\n","'_______________________________________ 0\n","s_______________________________________ 0\n","representative__________________________ 0\n","to______________________________________ 0\n","the_____________________________________ 0\n","european________________________________ 3\n","union___________________________________ 4\n","'_______________________________________ 0\n","s_______________________________________ 0\n","veterinary______________________________ 0\n","committee_______________________________ 0\n","werner__________________________________ 1\n","z_______________________________________ 2\n","##wing__________________________________ 2\n","##mann__________________________________ 2\n","said____________________________________ 0\n","on______________________________________ 0\n","wednesday_______________________________ 0\n","consumers_______________________________ 0\n","should__________________________________ 0\n","buy_____________________________________ 0\n","sheep___________________________________ 0\n","##me____________________________________ 0\n","##at____________________________________ 0\n","from____________________________________ 0\n","countries_______________________________ 0\n","other___________________________________ 0\n","than____________________________________ 0\n","britain_________________________________ 5\n","until___________________________________ 0\n","the_____________________________________ 0\n","scientific______________________________ 0\n","advice__________________________________ 0\n","was_____________________________________ 0\n","clearer_________________________________ 0\n","._______________________________________ 0\n","[SEP]___________________________________ -100\n"]}]},{"cell_type":"code","source":["## Applying on entire data\n","tokenized_datasets = conll2003.map(tokenize_and_align_labels, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["973c9b38c7164a488461bb2cd35db219","e585ce6b3e9741ad9dd3d735e9a01595","b560dbf6790144b6a520b73fff113c99","1a04fd56de034f6880ed15890b7ea0c6","3b3a143e7dd34ab2bd6cf87b6e15808c","ba0d9aa2195d43e487204a9e3ed3e28c","040d34940aca446a8fe4f324f46d3c2d","5fb89a51a79445b18ee64e3d0f313fc3","65cb5a181b2d49e78d2e312e9e2a5ac6","cf192e8b8d1140579016d90155e46c90","ed92831ab8c848f1b8e12182add4f5aa","bdcb0eaa742d42b295ce21eae94f7ee1","911d57e1bd164e84ad9893f52041f1c0","6ef20a0ba4d846fda683013803d442cf","16d22bd9c4f1430783454299aee7c193","f3d37eb7e1af417bb2c5108d72397ecf","d84ed0e6975041d6aae546ba974104ca","35ab1fe9720a4687bd8ba1224cb4b9da","30dbf9df912e414bb4114bae63da9587","f43d02d7e6d44a5b8b443371c1ee8757","2c5399d9c85044989f20e00a22a51804","6422a8b270d6490389d0cb605e6f56f3","df3330bd6a6e42a0ab88b1eb1d2d8ac6","742c48f1c64046a184474777f64a9042","745700c3549548d78c12c62cf0f25362","e137f2f2c3044273ad575290d878129a","f78a638da933436285f07db8589b96e8","9586614f837e4153a6b3de9ce571a091","8eaa2937790542c3852e9db4a832f9d1","135afbd4361641fd87cf513815490826","55a433e9503646588d25182365710465","98020577ada5436f97d5cd3c884734ab","da8df9c5391b4da0acba77a88999b045"]},"id":"4s2hs4WqEprL","executionInfo":{"status":"ok","timestamp":1669468692021,"user_tz":-360,"elapsed":5461,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"a1bb8c3f-25cd-4673-c3d4-9cca1cfe867b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/15 [00:00"],"text/html":["\n","
\n"," \n"," \n"," [2634/2634 08:38, Epoch 3/3]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
EpochTraining LossValidation LossPrecisionRecallF1Accuracy
10.2153000.0624590.9274510.9324310.9299340.983367
20.0496000.0548150.9344240.9452960.9398290.985925
30.0251000.0558620.9401480.9506660.9453780.986735

"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to test-ner/checkpoint-500\n","Configuration saved in test-ner/checkpoint-500/config.json\n","Model weights saved in test-ner/checkpoint-500/pytorch_model.bin\n","tokenizer config file saved in test-ner/checkpoint-500/tokenizer_config.json\n","Special tokens file saved in test-ner/checkpoint-500/special_tokens_map.json\n","The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: chunk_tags, id, tokens, ner_tags, pos_tags. If chunk_tags, id, tokens, ner_tags, pos_tags are not expected by `BertForTokenClassification.forward`, you can safely ignore this message.\n","***** Running Evaluation *****\n"," Num examples = 3250\n"," Batch size = 16\n","Saving model checkpoint to test-ner/checkpoint-1000\n","Configuration saved in test-ner/checkpoint-1000/config.json\n","Model weights saved in test-ner/checkpoint-1000/pytorch_model.bin\n","tokenizer config file saved in test-ner/checkpoint-1000/tokenizer_config.json\n","Special tokens file saved in test-ner/checkpoint-1000/special_tokens_map.json\n","Saving model checkpoint to test-ner/checkpoint-1500\n","Configuration saved in test-ner/checkpoint-1500/config.json\n","Model weights saved in test-ner/checkpoint-1500/pytorch_model.bin\n","tokenizer config file saved in test-ner/checkpoint-1500/tokenizer_config.json\n","Special tokens file saved in test-ner/checkpoint-1500/special_tokens_map.json\n","The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: chunk_tags, id, tokens, ner_tags, pos_tags. If chunk_tags, id, tokens, ner_tags, pos_tags are not expected by `BertForTokenClassification.forward`, you can safely ignore this message.\n","***** Running Evaluation *****\n"," Num examples = 3250\n"," Batch size = 16\n","Saving model checkpoint to test-ner/checkpoint-2000\n","Configuration saved in test-ner/checkpoint-2000/config.json\n","Model weights saved in test-ner/checkpoint-2000/pytorch_model.bin\n","tokenizer config file saved in test-ner/checkpoint-2000/tokenizer_config.json\n","Special tokens file saved in test-ner/checkpoint-2000/special_tokens_map.json\n","Saving model checkpoint to test-ner/checkpoint-2500\n","Configuration saved in test-ner/checkpoint-2500/config.json\n","Model weights saved in test-ner/checkpoint-2500/pytorch_model.bin\n","tokenizer config file saved in test-ner/checkpoint-2500/tokenizer_config.json\n","Special tokens file saved in test-ner/checkpoint-2500/special_tokens_map.json\n","The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: chunk_tags, id, tokens, ner_tags, pos_tags. If chunk_tags, id, tokens, ner_tags, pos_tags are not expected by `BertForTokenClassification.forward`, you can safely ignore this message.\n","***** Running Evaluation *****\n"," Num examples = 3250\n"," Batch size = 16\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=2634, training_loss=0.07699133603972926, metrics={'train_runtime': 521.0985, 'train_samples_per_second': 80.835, 'train_steps_per_second': 5.055, 'total_flos': 1021316467278600.0, 'train_loss': 0.07699133603972926, 'epoch': 3.0})"]},"metadata":{},"execution_count":35}]},{"cell_type":"markdown","source":["# Save"],"metadata":{"id":"4uAJII5_MApi"}},{"cell_type":"code","source":["## Save model\n","model.save_pretrained(\"ner_model\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zKcrftHYJGC5","executionInfo":{"status":"ok","timestamp":1669470587825,"user_tz":-360,"elapsed":2364,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"fdbc3323-750a-4afa-cf5d-641ec9413e5c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Configuration saved in ner_model/config.json\n","Model weights saved in ner_model/pytorch_model.bin\n"]}]},{"cell_type":"code","source":["## Save tokenizer\n","tokenizer.save_pretrained(\"tokenizer\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7LShwRx9JGAJ","executionInfo":{"status":"ok","timestamp":1669470606443,"user_tz":-360,"elapsed":778,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"0f42d35f-1d23-4f28-85ce-2e4450d584e3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["tokenizer config file saved in tokenizer/tokenizer_config.json\n","Special tokens file saved in tokenizer/special_tokens_map.json\n"]},{"output_type":"execute_result","data":{"text/plain":["('tokenizer/tokenizer_config.json',\n"," 'tokenizer/special_tokens_map.json',\n"," 'tokenizer/vocab.txt',\n"," 'tokenizer/added_tokens.json',\n"," 'tokenizer/tokenizer.json')"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","source":["id2label = {\n"," str(i): label for i,label in enumerate(label_list)\n","}\n","label2id = {\n"," label: str(i) for i,label in enumerate(label_list)\n","}"],"metadata":{"id":"HP6ULYHBL0yx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["id2label"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-Hbu7romL0wp","executionInfo":{"status":"ok","timestamp":1669470639695,"user_tz":-360,"elapsed":5,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"f6833a48-e9bd-42d8-83b0-12983a04c683"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'0': 'O',\n"," '1': 'B-PER',\n"," '2': 'I-PER',\n"," '3': 'B-ORG',\n"," '4': 'I-ORG',\n"," '5': 'B-LOC',\n"," '6': 'I-LOC',\n"," '7': 'B-MISC',\n"," '8': 'I-MISC'}"]},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["label2id"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YTCpYDNeL0uZ","executionInfo":{"status":"ok","timestamp":1669470639695,"user_tz":-360,"elapsed":3,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"ff447aec-b796-462b-f2f7-04eeda0c3041"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'O': '0',\n"," 'B-PER': '1',\n"," 'I-PER': '2',\n"," 'B-ORG': '3',\n"," 'I-ORG': '4',\n"," 'B-LOC': '5',\n"," 'I-LOC': '6',\n"," 'B-MISC': '7',\n"," 'I-MISC': '8'}"]},"metadata":{},"execution_count":40}]},{"cell_type":"markdown","source":["## Loading model & prediction"],"metadata":{"id":"rxDQ22YJNJ3a"}},{"cell_type":"code","source":["import json"],"metadata":{"id":"-1lc4YM9NE0a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["config = json.load(open(\"ner_model/config.json\"))\n","config[\"id2label\"] = id2label\n","config[\"label2id\"] = label2id\n","json.dump(config, open(\"ner_model/config.json\",\"w\"))"],"metadata":{"id":"q1eq3U-iNQIy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model_fine_tuned = AutoModelForTokenClassification.from_pretrained(\"ner_model\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z_KUts75NbEM","executionInfo":{"status":"ok","timestamp":1669470781011,"user_tz":-360,"elapsed":1612,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"947624ca-3d51-4801-f9bb-b3c6c26e56d9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["loading configuration file ner_model/config.json\n","Model config BertConfig {\n"," \"_name_or_path\": \"ner_model\",\n"," \"architectures\": [\n"," \"BertForTokenClassification\"\n"," ],\n"," \"attention_probs_dropout_prob\": 0.1,\n"," \"classifier_dropout\": null,\n"," \"gradient_checkpointing\": false,\n"," \"hidden_act\": \"gelu\",\n"," \"hidden_dropout_prob\": 0.1,\n"," \"hidden_size\": 768,\n"," \"id2label\": {\n"," \"0\": \"O\",\n"," \"1\": \"B-PER\",\n"," \"2\": \"I-PER\",\n"," \"3\": \"B-ORG\",\n"," \"4\": \"I-ORG\",\n"," \"5\": \"B-LOC\",\n"," \"6\": \"I-LOC\",\n"," \"7\": \"B-MISC\",\n"," \"8\": \"I-MISC\"\n"," },\n"," \"initializer_range\": 0.02,\n"," \"intermediate_size\": 3072,\n"," \"label2id\": {\n"," \"B-LOC\": \"5\",\n"," \"B-MISC\": \"7\",\n"," \"B-ORG\": \"3\",\n"," \"B-PER\": \"1\",\n"," \"I-LOC\": \"6\",\n"," \"I-MISC\": \"8\",\n"," \"I-ORG\": \"4\",\n"," \"I-PER\": \"2\",\n"," \"O\": \"0\"\n"," },\n"," \"layer_norm_eps\": 1e-12,\n"," \"max_position_embeddings\": 512,\n"," \"model_type\": \"bert\",\n"," \"num_attention_heads\": 12,\n"," \"num_hidden_layers\": 12,\n"," \"pad_token_id\": 0,\n"," \"position_embedding_type\": \"absolute\",\n"," \"torch_dtype\": \"float32\",\n"," \"transformers_version\": \"4.24.0\",\n"," \"type_vocab_size\": 2,\n"," \"use_cache\": true,\n"," \"vocab_size\": 30522\n","}\n","\n","loading weights file ner_model/pytorch_model.bin\n","All model checkpoint weights were used when initializing BertForTokenClassification.\n","\n","All the weights of BertForTokenClassification were initialized from the model checkpoint at ner_model.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForTokenClassification for predictions without further training.\n"]}]},{"cell_type":"code","source":["from transformers import pipeline"],"metadata":{"id":"OJKmTD1KNq5F"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nlp = pipeline(\"ner\", model=model_fine_tuned, tokenizer=tokenizer)\n","\n","\n","example = \"Bill Gates is the Founder of Microsoft\"\n","\n","ner_results = nlp(example)\n","\n","print(ner_results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TNLeXXvoNq2y","executionInfo":{"status":"ok","timestamp":1669470906357,"user_tz":-360,"elapsed":3,"user":{"displayName":"Boktiar Ahmed Bappy","userId":"10381972055342951581"}},"outputId":"8136cb00-95bf-42ac-c0b1-84b86073ca79"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[{'entity': 'B-PER', 'score': 0.9974279, 'index': 1, 'word': 'bill', 'start': 0, 'end': 4}, {'entity': 'I-PER', 'score': 0.99712795, 'index': 2, 'word': 'gates', 'start': 5, 'end': 10}, {'entity': 'B-ORG', 'score': 0.95886695, 'index': 7, 'word': 'microsoft', 'start': 29, 'end': 38}]\n"]}]},{"cell_type":"markdown","source":["Reference: https://huggingface.co/course/chapter7/2"],"metadata":{"id":"XDAn4l1FPBlL"}},{"cell_type":"code","source":[],"metadata":{"id":"0pd4D0KZNq0S"},"execution_count":null,"outputs":[]}]} -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # NLP-Projects-Notebooks 2 | --------------------------------------------------------------------------------