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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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base_model: bert-large-uncased |
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model-index: |
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- name: trainer |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: glue |
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type: glue |
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config: cola |
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split: validation |
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args: cola |
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metrics: |
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- type: accuracy |
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value: 0.8465963566634708 |
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name: Accuracy |
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- type: f1 |
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value: 0.8064540073113251 |
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name: F1 |
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- type: precision |
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value: 0.840606542828289 |
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name: Precision |
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- type: recall |
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value: 0.7876439727431708 |
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name: Recall |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# trainer |
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This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4490 |
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- Accuracy: 0.8466 |
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- F1: 0.8065 |
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- Precision: 0.8406 |
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- Recall: 0.7876 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 267 | 0.3860 | 0.8370 | 0.7999 | 0.8184 | 0.7876 | |
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| 0.3455 | 2.0 | 534 | 0.4490 | 0.8466 | 0.8065 | 0.8406 | 0.7876 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
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