End of training

README.md

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+---
+base_model: dmis-lab/biobert-v1.1
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: contrast_classifier_bio_bert
+  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. -->
+
+# contrast_classifier_bio_bert
+
+This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1097
+- Accuracy: 0.9857
+
+## 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: 12
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.0075        | 1.0   | 18   | 0.2757          | 0.9286   |
+| 0.0024        | 2.0   | 36   | 0.5377          | 0.8429   |
+| 0.0009        | 3.0   | 54   | 0.1979          | 0.9714   |
+| 0.0006        | 4.0   | 72   | 0.1630          | 0.9714   |
+| 0.0004        | 5.0   | 90   | 0.1114          | 0.9857   |
+| 0.0004        | 6.0   | 108  | 0.1033          | 0.9857   |
+| 0.0003        | 7.0   | 126  | 0.1036          | 0.9857   |
+| 0.0003        | 8.0   | 144  | 0.1067          | 0.9857   |
+| 0.0003        | 9.0   | 162  | 0.1095          | 0.9857   |
+| 0.0003        | 10.0  | 180  | 0.1094          | 0.9857   |
+| 0.0002        | 11.0  | 198  | 0.1096          | 0.9857   |
+| 0.0002        | 12.0  | 216  | 0.1097          | 0.9857   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.0
+- Tokenizers 0.15.0

config.json

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+{
+  "_name_or_path": "dmis-lab/biobert-v1.1",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "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": "no",
+    "1": "yes"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "no": 0,
+    "yes": 1
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

model.safetensors

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special_tokens_map.json

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+  "mask_token": "[MASK]",
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+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
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tokenizer_config.json

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+  "do_lower_case": false,
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+  "model_max_length": 512,
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+  "pad_token": "[PAD]",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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