alenatz/BioBERT-BioCause

README.md

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+---
+license: apache-2.0
+base_model: bert-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: biobert-biocause-trainer
+  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-biocause-trainer
+
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1681
+- Accuracy: 0.9485
+- F1: 0.9040
+- Recall: 0.9511
+- Precision: 0.8614
+
+## 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 | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.6094        | 0.16  | 50   | 0.5106          | 0.7701   | 0.6246 | 0.7492 | 0.5355    |
+| 0.5744        | 0.32  | 100  | 0.4291          | 0.8132   | 0.6898 | 0.8139 | 0.5986    |
+| 0.5282        | 0.48  | 150  | 0.3735          | 0.7963   | 0.6995 | 0.9290 | 0.5610    |
+| 0.4704        | 0.64  | 200  | 0.4850          | 0.8965   | 0.7724 | 0.6877 | 0.8808    |
+| 0.4809        | 0.8   | 250  | 0.2955          | 0.9074   | 0.8192 | 0.8218 | 0.8166    |
+| 0.3985        | 0.96  | 300  | 0.2699          | 0.8829   | 0.8014 | 0.9259 | 0.7064    |
+| 0.347         | 1.13  | 350  | 0.2695          | 0.9275   | 0.8587 | 0.8628 | 0.8547    |
+| 0.3729        | 1.29  | 400  | 0.2227          | 0.9320   | 0.8723 | 0.9101 | 0.8374    |
+| 0.4059        | 1.45  | 450  | 0.2130          | 0.9420   | 0.8894 | 0.9132 | 0.8668    |
+| 0.3023        | 1.61  | 500  | 0.1996          | 0.9477   | 0.8989 | 0.9117 | 0.8865    |
+| 0.2676        | 1.77  | 550  | 0.1814          | 0.9521   | 0.9074 | 0.9196 | 0.8955    |
+| 0.4202        | 1.93  | 600  | 0.1702          | 0.9452   | 0.8987 | 0.9511 | 0.8517    |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.3.1
+- Datasets 2.19.1
+- Tokenizers 0.15.1

config.json

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+{
+  "_name_or_path": "bert-base-cased",
+  "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,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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
+}

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model.safetensors

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training_args.bin

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