Training in progress epoch 0
Browse files- README.md +58 -0
- config.json +50 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
- vocab.txt +0 -0
README.md
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---
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license: mit
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base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
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tags:
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- generated_from_keras_callback
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model-index:
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- name: Kikia26/FineTunePubMedBertWithTensorflowKeras3
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results: []
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# Kikia26/FineTunePubMedBertWithTensorflowKeras3
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This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 1.4820
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- Validation Loss: 0.8904
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- Train Precision: 0.0
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- Train Recall: 0.0
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- Train F1: 0.0
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- Train Accuracy: 0.7808
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- Epoch: 0
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 200, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
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|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
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| 1.4820 | 0.8904 | 0.0 | 0.0 | 0.0 | 0.7808 | 0 |
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### Framework versions
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- Transformers 4.35.2
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- TensorFlow 2.14.0
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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config.json
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{
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"_name_or_path": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
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"architectures": [
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"BertForTokenClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "I-CONDITION",
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"1": "I-TEST",
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"2": "B-CONDITION",
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"3": "I-PATIENT_GROUP",
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"4": "B-ASSOCIATED_PROBLEM",
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"5": "O",
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"6": "I-ASSOCIATED_PROBLEM",
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"7": "B-INTERVENTION",
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"8": "B-PATIENT_GROUP",
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"9": "I-INTERVENTION",
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"10": "B-TEST"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"B-ASSOCIATED_PROBLEM": 4,
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"B-CONDITION": 2,
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"B-INTERVENTION": 7,
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"B-PATIENT_GROUP": 8,
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"B-TEST": 10,
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"I-ASSOCIATED_PROBLEM": 6,
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"I-CONDITION": 0,
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"I-INTERVENTION": 9,
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"I-PATIENT_GROUP": 3,
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"I-TEST": 1,
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"O": 5
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.35.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:bd606b4edf904eb0d7941c04f6d3149ce739919f4c29c3cb72aceeb82df2a866
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size 435873780
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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