---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: mixed_model_combined_data
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/yassmenyoussef55-arete-global/huggingface/runs/4lnhrv4o)
# mixed_model_combined_data

This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3112
- Accuracy: 0.8954
- F1: 0.8944
- Recall: 0.8954
- Precision: 0.8953

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 849
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.66          | 0.9982 | 212  | 0.7179          | 0.7648   | 0.7546 | 0.7648 | 0.7864    |
| 0.4943        | 1.9965 | 424  | 0.5750          | 0.8136   | 0.8106 | 0.8136 | 0.8352    |
| 0.2822        | 2.9994 | 637  | 0.3672          | 0.8713   | 0.8696 | 0.8713 | 0.8743    |
| 0.1882        | 3.9976 | 849  | 0.3112          | 0.8954   | 0.8944 | 0.8954 | 0.8953    |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1