---
library_name: transformers
license: mit
base_model: openai-gpt
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- precision
model-index:
- name: gpt1_sst2_right
  results: []
datasets:
- nyu-mll/glue
- stanfordnlp/sst2
---

<!-- 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. -->

# gpt1_sst2_right

This model is a fine-tuned version of [openai-gpt](https://huggingface.co/openai-gpt) on sst2 dataset of GLUE benchmark.
It achieves the following results on the evaluation set:
- Loss: 0.4216
- Accuracy: 0.9255
- Recall: 0.9369
- Precision: 0.9183

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.
Access to [Repository](https://github.com/GoktugGuvercin/Text-Classification/blob/main/gpt1_sst2.ipynb) for finetuning.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

For batched training, \<pad\> token is added to the tokenizer and the following padding-truncation options are adapted:

- Padding Side: "right"
- Truncation Side: "right"

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|
| 0.2           | 1.0   | 4210  | 0.2958          | 0.9037   | 0.8649 | 0.9412    |
| 0.1455        | 2.0   | 8420  | 0.3172          | 0.9186   | 0.9505 | 0.8960    |
| 0.0892        | 3.0   | 12630 | 0.3637          | 0.9278   | 0.9257 | 0.9320    |
| 0.0584        | 4.0   | 16840 | 0.4216          | 0.9255   | 0.9369 | 0.9183    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0