---
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
tags:
- generated_from_trainer
metrics:
- f1
- recall
- accuracy
- precision
model-index:
- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss
  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. -->

# mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss

This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0473
- F1: 0.7427
- Recall: 0.7662
- Accuracy: 0.7662
- Precision: 0.7444

## 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: 2e-05
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Recall | Accuracy | Precision |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:--------:|:---------:|
| 3.4822        | 1.0   | 883  | 2.0495          | 0.3963 | 0.4790 | 0.4790   | 0.3791    |
| 1.6347        | 2.0   | 1766 | 1.2672          | 0.6622 | 0.7030 | 0.7030   | 0.6535    |
| 1.0807        | 3.0   | 2649 | 1.0711          | 0.7172 | 0.7420 | 0.7420   | 0.7065    |
| 0.8958        | 4.0   | 3532 | 1.0654          | 0.7232 | 0.7489 | 0.7489   | 0.7218    |
| 0.7766        | 5.0   | 4415 | 1.0473          | 0.7427 | 0.7662 | 0.7662   | 0.7444    |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3