alenatz/cause-biobert-biocause

README.md

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+---
+base_model: dmis-lab/biobert-base-cased-v1.2
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: cause-biobert-biocause
+ 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. -->
+
+# cause-biobert-biocause
+
+This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5157
+- Precision: 0.2230
+- Recall: 0.4277
+- F1: 0.2931
+- Accuracy: 0.8241
+- Cause P: 0.2230
+- Cause R: 0.4277
+- Cause F1: 0.2931
+
+## 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: 3e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Cause P | Cause R | Cause F1 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:-------:|:--------:|
+| 0.6993 | 0.25 | 20 | 0.6314 | 0.0556 | 0.1698 | 0.0837 | 0.7587 | 0.0556 | 0.1698 | 0.0837 |
+| 0.6993 | 0.5 | 40 | 0.5747 | 0.0826 | 0.2327 | 0.1219 | 0.6524 | 0.0826 | 0.2327 | 0.1219 |
+| 0.6993 | 0.75 | 60 | 0.4896 | 0.1086 | 0.3899 | 0.1699 | 0.7420 | 0.1086 | 0.3899 | 0.1699 |
+| 0.6993 | 1.0 | 80 | 0.4554 | 0.1497 | 0.3145 | 0.2028 | 0.7840 | 0.1497 | 0.3145 | 0.2028 |
+| 0.6993 | 1.25 | 100 | 0.4952 | 0.1980 | 0.3774 | 0.2597 | 0.8353 | 0.1980 | 0.3774 | 0.2597 |
+| 0.6993 | 1.5 | 120 | 0.4837 | 0.1749 | 0.3774 | 0.2390 | 0.7984 | 0.1749 | 0.3774 | 0.2390 |
+| 0.6993 | 1.75 | 140 | 0.4786 | 0.1873 | 0.4088 | 0.2569 | 0.7991 | 0.1873 | 0.4088 | 0.2569 |
+| 0.6993 | 2.0 | 160 | 0.5157 | 0.2230 | 0.4277 | 0.2931 | 0.8241 | 0.2230 | 0.4277 | 0.2931 |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.3.1.post100
+- Datasets 2.20.0
+- Tokenizers 0.15.1

config.json

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+{
+ "_name_or_path": "dmis-lab/biobert-base-cased-v1.2",
+ "architectures": [
+ "BertForUnbalancedTokenClassification"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "classifier_dropout": null,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 768,
+ "id2label": {
+ "0": "O",
+ "1": "I-CAU"
+ },
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "label2id": {
+ "I-CAU": 1,
+ "O": 0
+ },
+ "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",
+ "torch_dtype": "float32",
+ "transformers_version": "4.37.2",
+ "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]",
+ "pad_token": "[PAD]",
+ "sep_token": "[SEP]",
+ "unk_token": "[UNK]"
+}

tokenizer_config.json

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+ "added_tokens_decoder": {
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+ "tokenizer_class": "BertTokenizer",
+ "unk_token": "[UNK]"
+}

training_args.bin

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