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Training complete

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  1. README.md +64 -0
  2. config.json +39 -0
  3. model.safetensors +3 -0
  4. tokenizer.json +0 -0
  5. tokenizer_config.json +15 -0
  6. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
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+ tags:
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+ - classification
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: clasificador-medical-biomedbert
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # clasificador-medical-biomedbert
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+
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+ This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9047
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+ - Accuracy: 0.6514
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 3
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 405 | 0.7987 | 0.6264 |
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+ | 0.8499 | 2.0 | 810 | 0.8230 | 0.6333 |
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+ | 0.5600 | 3.0 | 1215 | 0.9047 | 0.6514 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 5.7.0
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+ - Pytorch 2.10.0+cu128
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+ - Datasets 4.8.5
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+ - Tokenizers 0.22.2
config.json ADDED
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+ {
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+ "add_cross_attention": false,
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+ "architectures": [
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+ "BertForSequenceClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "bos_token_id": null,
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+ "classifier_dropout": null,
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+ "dtype": "float32",
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+ "eos_token_id": 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": "negative",
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+ "1": "neutral",
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+ "2": "positive"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 3072,
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+ "is_decoder": false,
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+ "label2id": {
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+ "negative": 0,
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+ "neutral": 1,
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+ "positive": 2
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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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+ "problem_type": "single_label_classification",
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+ "tie_word_embeddings": true,
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+ "transformers_version": "5.7.0",
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+ "type_vocab_size": 2,
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+ "use_cache": false,
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+ "vocab_size": 30522
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+ }
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+ "tokenizer_class": "BertTokenizer",
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