---
license: mit
base_model: vicgalle/xlm-roberta-large-xnli-anli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-xnli-anli-v5.0
  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. -->

# xlm-roberta-large-xnli-anli-v5.0

This model is a fine-tuned version of [vicgalle/xlm-roberta-large-xnli-anli](https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5120
- F1 Macro: 0.8215
- F1 Micro: 0.8223
- Accuracy Balanced: 0.8216
- Accuracy: 0.8223
- Precision Macro: 0.8215
- Recall Macro: 0.8216
- Precision Micro: 0.8223
- Recall Micro: 0.8223

## 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: 9e-06
- train_batch_size: 8
- eval_batch_size: 64
- seed: 40
- 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_ratio: 0.06
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.3779        | 0.85  | 200  | 0.4494          | 0.8020   | 0.8020   | 0.8084            | 0.8020   | 0.8088          | 0.8084       | 0.8020          | 0.8020       |
| 0.2646        | 1.69  | 400  | 0.4425          | 0.8113   | 0.8121   | 0.8126            | 0.8121   | 0.8108          | 0.8126       | 0.8121          | 0.8121       |
| 0.1961        | 2.54  | 600  | 0.5222          | 0.8131   | 0.8147   | 0.8129            | 0.8147   | 0.8135          | 0.8129       | 0.8147          | 0.8147       |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3