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--- |
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library_name: transformers |
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license: mit |
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base_model: openai-gpt |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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model-index: |
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- name: gpt1_sst2_right |
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results: [] |
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datasets: |
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- nyu-mll/glue |
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- stanfordnlp/sst2 |
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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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# gpt1_sst2_right |
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This model is a fine-tuned version of [openai-gpt](https://huggingface.co/openai-gpt) on sst2 dataset of GLUE benchmark. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4216 |
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- Accuracy: 0.9255 |
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- Recall: 0.9369 |
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- Precision: 0.9183 |
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For testing, the model is loaded as a pipeline, and used for the prediction of each sample in test split. The samples and their predictions are recorded in [test_preds.csv](https://huggingface.co/goktug14/gpt1_sst2_right/blob/main/test_preds.csv) file. |
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Access to [Repository](https://github.com/GoktugGuvercin/Text-Classification/blob/main/gpt1_sst2.ipynb) for finetuning. |
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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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For batched training, \<pad\> token is added to the tokenizer and the following padding-truncation options are adapted: |
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- Padding Side: "right" |
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- Truncation Side: "right" |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:| |
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| 0.2 | 1.0 | 4210 | 0.2958 | 0.9037 | 0.8649 | 0.9412 | |
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| 0.1455 | 2.0 | 8420 | 0.3172 | 0.9186 | 0.9505 | 0.8960 | |
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| 0.0892 | 3.0 | 12630 | 0.3637 | 0.9278 | 0.9257 | 0.9320 | |
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| 0.0584 | 4.0 | 16840 | 0.4216 | 0.9255 | 0.9369 | 0.9183 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
