End of training

README.md

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+---
+tags:
+- generated_from_trainer
+model-index:
+- name: ner_model_ep3
+  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_ep3
+
+This model was trained from scratch on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3874
+-  allergy Name F1: 0.7968
+-  allergy Name Pres: 0.7706
+-  allergy Name Rec: 0.8249
+-  cancer F1: 0.7556
+-  cancer Pres: 0.7589
+-  cancer Rec: 0.7524
+-  chronic Disease F1: 0.7776
+-  chronic Disease Pres: 0.7562
+-  chronic Disease Rec: 0.8002
+-  treatment F1: 0.7804
+-  treatmen Prest: 0.7620
+-  treatment Rec: 0.7996
+- Over All Precision: 0.7596
+- Over All Recall: 0.7936
+- Over All F1: 0.7762
+- Over All Accuracy: 0.8806
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:----------------:|:------------------:|:-----------------:|:----------:|:------------:|:-----------:|:-------------------:|:---------------------:|:--------------------:|:-------------:|:---------------:|:--------------:|:------------------:|:---------------:|:-----------:|:-----------------:|
+| 0.3761        | 1.0   | 324  | 0.3480          | 0.7346           | 0.6720             | 0.8099            | 0.7108     | 0.7584       | 0.6688      | 0.7657              | 0.7619                | 0.7695               | 0.7700        | 0.7437          | 0.7983         | 0.7499             | 0.7687          | 0.7592      | 0.8738            |
+| 0.29          | 2.0   | 648  | 0.3548          | 0.7593           | 0.7023             | 0.8263            | 0.7406     | 0.7683       | 0.7149      | 0.7710              | 0.7608                | 0.7816               | 0.7738        | 0.7435          | 0.8067         | 0.7519             | 0.7842          | 0.7677      | 0.8775            |
+| 0.232         | 3.0   | 972  | 0.3579          | 0.8046           | 0.7787             | 0.8323            | 0.7446     | 0.7472       | 0.7421      | 0.7763              | 0.7568                | 0.7968               | 0.7798        | 0.7658          | 0.7944         | 0.7601             | 0.7887          | 0.7741      | 0.8809            |
+| 0.1945        | 4.0   | 1296 | 0.3829          | 0.7942           | 0.7645             | 0.8263            | 0.7463     | 0.7678       | 0.7260      | 0.7749              | 0.7584                | 0.7920               | 0.7808        | 0.7683          | 0.7938         | 0.7643             | 0.7840          | 0.7741      | 0.8792            |
+| 0.1734        | 5.0   | 1620 | 0.3874          | 0.7968           | 0.7706             | 0.8249            | 0.7556     | 0.7589       | 0.7524      | 0.7776              | 0.7562                | 0.8002               | 0.7804        | 0.7620          | 0.7996         | 0.7596             | 0.7936          | 0.7762      | 0.8806            |
+
+
+### 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_ep2",
+  "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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runs/Jul21_17-52-52_3f8ac3307322/events.out.tfevents.1721584373.3f8ac3307322.181.7

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special_tokens_map.json

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

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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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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