End of training

README.md

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+---
+library_name: transformers
+license: cc-by-4.0
+base_model: NazaGara/NER-fine-tuned-BETO
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: biobert-ner-finetuned
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# biobert-ner-finetuned
+
+This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0988
+- Precision: 0.9505
+- Recall: 0.9665
+- F1: 0.9584
+- Accuracy: 0.9778
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 12
+- eval_batch_size: 12
+- seed: 42
+- gradient_accumulation_steps: 3
+- total_train_batch_size: 36
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3897        | 1.0   | 272  | 0.2170          | 0.8909    | 0.8755 | 0.8831 | 0.9430   |
+| 0.146         | 2.0   | 544  | 0.1118          | 0.9242    | 0.9641 | 0.9438 | 0.9710   |
+| 0.0929        | 3.0   | 816  | 0.1015          | 0.9403    | 0.9648 | 0.9524 | 0.9755   |
+| 0.0794        | 4.0   | 1088 | 0.0988          | 0.9505    | 0.9665 | 0.9584 | 0.9778   |
+| 0.0576        | 5.0   | 1360 | 0.1028          | 0.9502    | 0.9658 | 0.9580 | 0.9772   |
+| 0.0439        | 6.0   | 1632 | 0.1088          | 0.9450    | 0.9640 | 0.9544 | 0.9759   |
+| 0.0343        | 7.0   | 1904 | 0.1063          | 0.9479    | 0.9683 | 0.9580 | 0.9777   |
+| 0.0314        | 8.0   | 2176 | 0.1117          | 0.9481    | 0.9678 | 0.9578 | 0.9777   |
+| 0.0275        | 9.0   | 2448 | 0.1122          | 0.9512    | 0.9655 | 0.9583 | 0.9781   |
+| 0.0218        | 10.0  | 2720 | 0.1155          | 0.9512    | 0.9656 | 0.9583 | 0.9781   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu118
+- Datasets 3.1.0
+- Tokenizers 0.20.3

final_model/config.json

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+{
+  "_name_or_path": "NazaGara/NER-fine-tuned-BETO",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "B_CANCER_CONCEPT",
+    "1": "B_CHEMOTHERAPY",
+    "2": "B_DATE",
+    "3": "B_DRUG",
+    "4": "B_FAMILY",
+    "5": "B_FREQ",
+    "6": "B_IMPLICIT_DATE",
+    "7": "B_INTERVAL",
+    "8": "B_METRIC",
+    "9": "B_OCURRENCE_EVENT",
+    "10": "B_QUANTITY",
+    "11": "B_RADIOTHERAPY",
+    "12": "B_SMOKER_STATUS",
+    "13": "B_STAGE",
+    "14": "B_SURGERY",
+    "15": "B_TNM",
+    "16": "I_CANCER_CONCEPT",
+    "17": "I_DATE",
+    "18": "I_DRUG",
+    "19": "I_FAMILY",
+    "20": "I_FREQ",
+    "21": "I_IMPLICIT_DATE",
+    "22": "I_INTERVAL",
+    "23": "I_METRIC",
+    "24": "I_OCURRENCE_EVENT",
+    "25": "I_SMOKER_STATUS",
+    "26": "I_STAGE",
+    "27": "I_SURGERY",
+    "28": "I_TNM",
+    "29": "O"
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+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "B_CHEMOTHERAPY": 1,
+    "B_DATE": 2,
+    "B_DRUG": 3,
+    "B_FAMILY": 4,
+    "B_FREQ": 5,
+    "B_IMPLICIT_DATE": 6,
+    "B_INTERVAL": 7,
+    "B_METRIC": 8,
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+    "B_QUANTITY": 10,
+    "B_RADIOTHERAPY": 11,
+    "B_SMOKER_STATUS": 12,
+    "B_STAGE": 13,
+    "B_SURGERY": 14,
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+    "I_DATE": 17,
+    "I_DRUG": 18,
+    "I_FAMILY": 19,
+    "I_FREQ": 20,
+    "I_IMPLICIT_DATE": 21,
+    "I_INTERVAL": 22,
+    "I_METRIC": 23,
+    "I_OCURRENCE_EVENT": 24,
+    "I_SMOKER_STATUS": 25,
+    "I_STAGE": 26,
+    "I_SURGERY": 27,
+    "I_TNM": 28,
+    "O": 29
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 31002
+}

final_model/pytorch_model.bin

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final_model/special_tokens_map.json

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final_model/tokenizer_config.json

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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
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+  "model_max_length": 512,
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+  "num_labels": 9,
+  "pad_token": "[PAD]",
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+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

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