---
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: sentiment_deberta
  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. -->

# sentiment_deberta

This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6252
- Accuracy: 0.7418
- F1: 0.6848
- Precision: 0.6668
- Recall: 0.7317

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7168        | 1.0   | 94   | 0.8071          | 0.6421   | 0.6022 | 0.6012    | 0.6733 |
| 0.6442        | 2.0   | 188  | 0.6195          | 0.7428   | 0.6789 | 0.6617    | 0.7176 |
| 0.5657        | 3.0   | 282  | 0.7655          | 0.6615   | 0.6319 | 0.6301    | 0.7172 |
| 0.5001        | 4.0   | 376  | 0.6058          | 0.7465   | 0.6896 | 0.6717    | 0.7352 |
| 0.5145        | 5.0   | 470  | 0.6252          | 0.7418   | 0.6848 | 0.6668    | 0.7317 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1