End of training

README.md

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+---
+tags:
+- generated_from_trainer
+model-index:
+- name: ner_model_ep2
+  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. -->
+
+# ner_model_ep2
+
+This model was trained from scratch on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3762
+-  allergy Name F1: 0.7874
+-  allergy Name Pres: 0.7751
+-  allergy Name Rec: 0.8
+-  cancer F1: 0.7143
+-  cancer Pres: 0.7106
+-  cancer Rec: 0.7180
+-  chronic Disease F1: 0.7611
+-  chronic Disease Pres: 0.7583
+-  chronic Disease Rec: 0.7638
+-  treatment F1: 0.7718
+-  treatmen Prest: 0.7533
+-  treatment Rec: 0.7913
+- Over All Precision: 0.7495
+- Over All Recall: 0.7704
+- Over All F1: 0.7598
+- Over All Accuracy: 0.8780
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |  allergy Name F1 |  allergy Name Pres |  allergy Name Rec |  cancer F1 |  cancer Pres |  cancer Rec |  chronic Disease F1 |  chronic Disease Pres |  chronic Disease Rec |  treatment F1 |  treatmen Prest |  treatment Rec | Over All Precision | Over All Recall | Over All F1 | Over All Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
+| 0.4256        | 1.0   | 329  | 0.3561          | 0.7266           | 0.6896             | 0.7679            | 0.6592     | 0.6972       | 0.6251      | 0.7232              | 0.7719                | 0.6804               | 0.7377        | 0.7443          | 0.7312         | 0.7457             | 0.6974          | 0.7207      | 0.8689            |
+| 0.3248        | 2.0   | 658  | 0.3547          | 0.7836           | 0.7895             | 0.7778            | 0.6873     | 0.6813       | 0.6933      | 0.7480              | 0.7509                | 0.7451               | 0.7517        | 0.7121          | 0.7959         | 0.7232             | 0.7613          | 0.7417      | 0.8723            |
+| 0.26          | 3.0   | 987  | 0.3655          | 0.7599           | 0.7196             | 0.8049            | 0.6904     | 0.6764       | 0.7050      | 0.7548              | 0.7620                | 0.7477               | 0.7672        | 0.7432          | 0.7928         | 0.7393             | 0.7633          | 0.7511      | 0.8753            |
+| 0.225         | 4.0   | 1316 | 0.3662          | 0.7878           | 0.7783             | 0.7975            | 0.7036     | 0.7308       | 0.6782      | 0.7603              | 0.7653                | 0.7554               | 0.7682        | 0.7504          | 0.7869         | 0.7539             | 0.7593          | 0.7566      | 0.8777            |
+| 0.1968        | 5.0   | 1645 | 0.3762          | 0.7874           | 0.7751             | 0.8               | 0.7143     | 0.7106       | 0.7180      | 0.7611              | 0.7583                | 0.7638               | 0.7718        | 0.7533          | 0.7913         | 0.7495             | 0.7704          | 0.7598      | 0.8780            |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "ner_model_ep1",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForTokenClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "O",
+    "1": "B-treatment",
+    "2": "B-chronic_disease",
+    "3": "I-chronic_disease",
+    "4": "I-treatment",
+    "5": "B-cancer",
+    "6": "I-cancer",
+    "7": "B-allergy_name",
+    "8": "I-allergy_name"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "B-allergy_name": 7,
+    "B-cancer": 5,
+    "B-chronic_disease": 2,
+    "B-treatment": 1,
+    "I-allergy_name": 8,
+    "I-cancer": 6,
+    "I-chronic_disease": 3,
+    "I-treatment": 4,
+    "O": 0
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.4",
+  "vocab_size": 30522
+}

model.safetensors

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special_tokens_map.json

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+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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