---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- generated_from_trainer
model-index:
- name: Mistral-7B-Instruct-v0.2-absa-MT-laptops
  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-7B-Instruct-v0.2-absa-MT-laptops

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0060

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 1200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8786        | 0.13  | 40   | 0.1392          |
| 0.0627        | 0.25  | 80   | 0.0165          |
| 0.0162        | 0.38  | 120  | 0.0143          |
| 0.0139        | 0.5   | 160  | 0.0125          |
| 0.0131        | 0.63  | 200  | 0.0110          |
| 0.0115        | 0.75  | 240  | 0.0106          |
| 0.0111        | 0.88  | 280  | 0.0105          |
| 0.0091        | 1.0   | 320  | 0.0093          |
| 0.0073        | 1.13  | 360  | 0.0090          |
| 0.0079        | 1.25  | 400  | 0.0090          |
| 0.0068        | 1.38  | 440  | 0.0083          |
| 0.0065        | 1.5   | 480  | 0.0076          |
| 0.0071        | 1.63  | 520  | 0.0076          |
| 0.0062        | 1.75  | 560  | 0.0077          |
| 0.0062        | 1.88  | 600  | 0.0069          |
| 0.0058        | 2.0   | 640  | 0.0069          |
| 0.0034        | 2.13  | 680  | 0.0070          |
| 0.0034        | 2.25  | 720  | 0.0066          |
| 0.0034        | 2.38  | 760  | 0.0071          |
| 0.0038        | 2.5   | 800  | 0.0064          |
| 0.0032        | 2.63  | 840  | 0.0070          |
| 0.0031        | 2.75  | 880  | 0.0062          |
| 0.0032        | 2.88  | 920  | 0.0058          |
| 0.0026        | 3.0   | 960  | 0.0059          |
| 0.0018        | 3.13  | 1000 | 0.0058          |
| 0.0014        | 3.26  | 1040 | 0.0059          |
| 0.0014        | 3.38  | 1080 | 0.0060          |
| 0.0012        | 3.51  | 1120 | 0.0060          |
| 0.0014        | 3.63  | 1160 | 0.0060          |
| 0.001         | 3.76  | 1200 | 0.0060          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2