notlath/BioBERT_Symptom2Disease_dataset

README.md

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+---
+library_name: transformers
+base_model: dmis-lab/biobert-v1.1
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: BioBERT_Symptom2Disease_dataset
+  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_Symptom2Disease_dataset
+
+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: 0.4577
+- Accuracy: 0.9867
+- F1 Macro: 0.9866
+- Precision Macro: 0.9868
+- Recall Macro: 0.9867
+
+## 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: 0.00010665073784797153
+- train_batch_size: 4
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 32
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 65
+- num_epochs: 10
+- label_smoothing_factor: 0.1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|
+| 10.2542       | 1.0   | 66   | 0.6162          | 0.9289   | 0.9297   | 0.9366          | 0.9289       |
+| 4.3535        | 2.0   | 132  | 0.5109          | 0.9644   | 0.9643   | 0.9667          | 0.9644       |
+| 3.8697        | 3.0   | 198  | 0.4679          | 0.9867   | 0.9867   | 0.9871          | 0.9867       |
+| 3.5071        | 4.0   | 264  | 0.4744          | 0.9822   | 0.9823   | 0.9826          | 0.9822       |
+| 3.3928        | 5.0   | 330  | 0.4572          | 0.9889   | 0.9888   | 0.9890          | 0.9889       |
+| 3.3732        | 6.0   | 396  | 0.4714          | 0.98     | 0.9800   | 0.9802          | 0.98         |
+| 3.3737        | 7.0   | 462  | 0.4599          | 0.9844   | 0.9844   | 0.9845          | 0.9844       |
+| 3.3634        | 8.0   | 528  | 0.4577          | 0.9867   | 0.9866   | 0.9868          | 0.9867       |
+
+
+### Framework versions
+
+- Transformers 5.3.0
+- Pytorch 2.11.0+cu130
+- Datasets 4.8.4
+- Tokenizers 0.22.2

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
+    "5": 5
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
+    "5": 5
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 28996
+}

model.safetensors

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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