---
license: mit
base_model: openai-community/gpt2-large
tags:
- generated_from_trainer
datasets:
- stanfordnlp/snli
metrics:
- accuracy
model-index:
- name: gpt2-large-snli-model3
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: snli
      type: stanfordnlp/snli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9145498882340988
---

<!-- 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. -->

# gpt2-large-snli-model3

This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large) on the snli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2891
- Accuracy: 0.9145

## 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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 87
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3125        | 1.0   | 4292  | 0.2509          | 0.9126   |
| 0.2233        | 2.0   | 8584  | 0.2487          | 0.9143   |
| 0.1424        | 3.0   | 12876 | 0.2891          | 0.9145   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0