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

This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4987
- F1 Macro: 0.8279
- F1 Micro: 0.8288
- Accuracy Balanced: 0.8278
- Accuracy: 0.8288
- Precision Macro: 0.8281
- Recall Macro: 0.8278
- Precision Micro: 0.8288
- Recall Micro: 0.8288

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.3851        | 0.85  | 200  | 0.4586          | 0.8017   | 0.8025   | 0.8029            | 0.8025   | 0.8012          | 0.8029       | 0.8025          | 0.8025       |
| 0.2689        | 1.69  | 400  | 0.4498          | 0.8137   | 0.8147   | 0.8145            | 0.8147   | 0.8133          | 0.8145       | 0.8147          | 0.8147       |
| 0.194         | 2.54  | 600  | 0.5334          | 0.8244   | 0.8253   | 0.8252            | 0.8253   | 0.8239          | 0.8252       | 0.8253          | 0.8253       |

### eval result
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.535|0.278|0.552|0.499|
|eval_f1_macro|0.817|0.916|0.823|0.828|
|eval_f1_micro|0.818|0.916|0.824|0.829|
|eval_accuracy_balanced|0.817|0.917|0.823|0.828|
|eval_accuracy|0.818|0.916|0.824|0.829|
|eval_precision_macro|0.817|0.917|0.823|0.828|
|eval_recall_macro|0.817|0.917|0.823|0.828|
|eval_precision_micro|0.818|0.916|0.824|0.829|
|eval_recall_micro|0.818|0.916|0.824|0.829|
|eval_runtime|50.89|0.639|11.177|44.352|
|eval_samples_per_second|167.026|1480.253|169.012|170.387|
|eval_steps_per_second|2.613|23.471|2.684|2.683|
|Size of dataset|8500|946|1889|7557|

### Framework versions

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