alenatz/BERT-BioCause-oversample

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: bert-because-trainer-oversample
+  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. -->
+
+# bert-because-trainer-oversample
+
+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: 0.3274
+- Accuracy: 0.8972
+- F1: 0.8299
+- Recall: 0.8342
+- Precision: 0.8256
+
+## 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: 5e-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: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.6383        | 0.07  | 25   | 0.5128          | 0.7461   | 0.2882 | 0.1710 | 0.9167    |
+| 0.5933        | 0.15  | 50   | 0.5335          | 0.7352   | 0.6545 | 0.8342 | 0.5385    |
+| 0.4774        | 0.22  | 75   | 0.4369          | 0.8131   | 0.5804 | 0.4301 | 0.8925    |
+| 0.4801        | 0.3   | 100  | 0.3538          | 0.8458   | 0.7429 | 0.7409 | 0.7448    |
+| 0.3765        | 0.37  | 125  | 0.3890          | 0.8536   | 0.7267 | 0.6477 | 0.8278    |
+| 0.3411        | 0.45  | 150  | 0.4052          | 0.8474   | 0.7710 | 0.8549 | 0.7021    |
+| 0.2802        | 0.52  | 175  | 0.3509          | 0.8660   | 0.7701 | 0.7461 | 0.7956    |
+| 0.2558        | 0.59  | 200  | 0.4704          | 0.8629   | 0.7179 | 0.5803 | 0.9412    |
+| 0.4603        | 0.67  | 225  | 0.3298          | 0.8801   | 0.7968 | 0.7824 | 0.8118    |
+| 0.3211        | 0.74  | 250  | 0.3053          | 0.8925   | 0.8189 | 0.8083 | 0.8298    |
+| 0.2475        | 0.82  | 275  | 0.3052          | 0.8879   | 0.8209 | 0.8549 | 0.7895    |
+| 0.2644        | 0.89  | 300  | 0.3688          | 0.8910   | 0.8077 | 0.7617 | 0.8596    |
+| 0.3206        | 0.96  | 325  | 0.3332          | 0.8988   | 0.8320 | 0.8342 | 0.8299    |
+
+
+### 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",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

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