---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: oops_i_did_it_again_eval_hans_full_set
  results: []
---

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# oops_i_did_it_again_eval_hans_full_set

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8314
- Precision: 0.7598
- Recall: 0.2665
- F1-score: 0.3946
- Accuracy: 0.5911

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.5414        | 1.0   | 24544 | 1.8314          | 0.7598    | 0.2665 | 0.3946   | 0.5911   |


### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2