---
tags:
- trl
- sft
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: mistral_sparse_80_percent_boolq_1000
  results: []
---

<!-- 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. -->

# mistral_sparse_80_percent_boolq_1000

This model is a fine-tuned version of [](https://huggingface.co/) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3381
- Accuracy: 0.8664

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 2
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4991        | 0.05  | 50   | 0.5522          | 0.7216   |
| 0.3812        | 0.1   | 100  | 0.4342          | 0.8141   |
| 0.369         | 0.15  | 150  | 0.4112          | 0.8170   |
| 0.4132        | 0.2   | 200  | 0.4139          | 0.8382   |
| 0.4219        | 0.25  | 250  | 0.3940          | 0.8339   |
| 0.4144        | 0.3   | 300  | 0.3803          | 0.8481   |
| 0.1534        | 0.35  | 350  | 0.3786          | 0.8516   |
| 0.4855        | 0.4   | 400  | 0.3821          | 0.8502   |
| 0.2109        | 0.45  | 450  | 0.3583          | 0.8516   |
| 0.3026        | 0.5   | 500  | 0.3675          | 0.8558   |
| 0.2903        | 0.55  | 550  | 0.3744          | 0.8537   |
| 0.2988        | 0.6   | 600  | 0.3573          | 0.8587   |
| 0.3432        | 0.65  | 650  | 0.3396          | 0.8657   |
| 0.3156        | 0.7   | 700  | 0.3299          | 0.8671   |
| 0.4978        | 0.75  | 750  | 0.3623          | 0.8657   |
| 0.4523        | 0.8   | 800  | 0.3240          | 0.8700   |
| 0.2367        | 0.85  | 850  | 0.3393          | 0.8678   |
| 0.3334        | 0.9   | 900  | 0.3252          | 0.8834   |
| 0.3286        | 0.95  | 950  | 0.3605          | 0.8742   |
| 0.1659        | 1.0   | 1000 | 0.3269          | 0.8742   |
| 0.2373        | 1.05  | 1050 | 0.3256          | 0.8792   |
| 0.5102        | 1.1   | 1100 | 0.3633          | 0.8749   |


### Framework versions

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