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--- |
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library_name: transformers |
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license: mit |
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base_model: mateiaassAI/teacher_sst2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- laroseda |
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metrics: |
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- f1 |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: teacher_sst2_laroseda |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: laroseda |
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type: laroseda |
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config: laroseda |
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split: train |
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args: laroseda |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.9489953582155384 |
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- name: Accuracy |
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type: accuracy |
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value: 0.949 |
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- name: Precision |
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type: precision |
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value: 0.9490837535014006 |
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- name: Recall |
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type: recall |
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value: 0.949 |
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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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# teacher_sst2_laroseda |
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This model is a fine-tuned version of [mateiaassAI/teacher_sst2](https://huggingface.co/mateiaassAI/teacher_sst2) on the laroseda dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1906 |
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- F1: 0.9490 |
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- Roc Auc: 0.9490 |
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- Accuracy: 0.949 |
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- Precision: 0.9491 |
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- Recall: 0.949 |
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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: 1.7e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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 | F1 | Roc Auc | Accuracy | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:---------:|:------:| |
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| 0.1799 | 1.0 | 688 | 0.1426 | 0.9435 | 0.9434 | 0.943 | 0.9441 | 0.943 | |
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| 0.1071 | 2.0 | 1376 | 0.1906 | 0.9490 | 0.9490 | 0.949 | 0.9491 | 0.949 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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