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
+datasets:
+- biobert_json
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: NER-finetuning-BETO-CM-V3
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: biobert_json
+      type: biobert_json
+      config: Biobert_json
+      split: validation
+      args: Biobert_json
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9326628771316134
+    - name: Recall
+      type: recall
+      value: 0.9424500165690931
+    - name: F1
+      type: f1
+      value: 0.9375309048953355
+    - name: Accuracy
+      type: accuracy
+      value: 0.977440285204991
+---
+
+<!-- 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. -->
+
+# NER-finetuning-BETO-CM-V3
+
+This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the biobert_json dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1452
+- Precision: 0.9327
+- Recall: 0.9425
+- F1: 0.9375
+- Accuracy: 0.9774
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- 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
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0459        | 1.0   | 1224  | 0.1312          | 0.9209    | 0.9487 | 0.9346 | 0.9721   |
+| 0.0457        | 2.0   | 2448  | 0.1208          | 0.9199    | 0.9584 | 0.9388 | 0.9761   |
+| 0.0473        | 3.0   | 3672  | 0.1092          | 0.9341    | 0.9527 | 0.9433 | 0.9797   |
+| 0.0335        | 4.0   | 4896  | 0.1123          | 0.9321    | 0.9482 | 0.9401 | 0.9774   |
+| 0.0299        | 5.0   | 6120  | 0.1190          | 0.9313    | 0.9475 | 0.9393 | 0.9768   |
+| 0.0273        | 6.0   | 7344  | 0.1224          | 0.9335    | 0.9444 | 0.9389 | 0.9774   |
+| 0.0208        | 7.0   | 8568  | 0.1354          | 0.9356    | 0.9463 | 0.9409 | 0.9782   |
+| 0.02          | 8.0   | 9792  | 0.1371          | 0.9302    | 0.9434 | 0.9368 | 0.9774   |
+| 0.0158        | 9.0   | 11016 | 0.1408          | 0.9320    | 0.9448 | 0.9383 | 0.9777   |
+| 0.0153        | 10.0  | 12240 | 0.1452          | 0.9327    | 0.9425 | 0.9375 | 0.9774   |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B_CANCER_CONCEPT": 0,
+    "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,
+    "B_OCURRENCE_EVENT": 9,
+    "B_QUANTITY": 10,
+    "B_RADIOTHERAPY": 11,
+    "B_SMOKER_STATUS": 12,
+    "B_STAGE": 13,
+    "B_SURGERY": 14,
+    "B_TNM": 15,
+    "I_CANCER_CONCEPT": 16,
+    "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
+}

model.safetensors

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special_tokens_map.json

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+    "lstrip": false,
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+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "num_labels": 9,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

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