---
license: mit
base_model: joeddav/xlm-roberta-large-xnli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-xnli-v3.0
  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-v3.0

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: 0.2434
- F1 Macro: 0.9286
- F1 Micro: 0.9287
- Accuracy Balanced: 0.9296
- Accuracy: 0.9287
- Precision Macro: 0.9288
- Recall Macro: 0.9296
- Precision Micro: 0.9287
- Recall Micro: 0.9287

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.2328        | 1.69  | 200  | 0.2811          | 0.8942   | 0.8942   | 0.8943            | 0.8942   | 0.8942          | 0.8943       | 0.8942          | 0.8942       |

### eval result
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|1.38|0.25|0.277|0.243|
|eval_f1_macro|0.583|0.925|0.91|0.929|
|eval_f1_micro|0.584|0.925|0.91|0.929|
|eval_accuracy_balanced|0.592|0.925|0.91|0.93|
|eval_accuracy|0.584|0.925|0.91|0.929|
|eval_precision_macro|0.595|0.925|0.91|0.929|
|eval_recall_macro|0.592|0.925|0.91|0.93|
|eval_precision_micro|0.584|0.925|0.91|0.929|
|eval_recall_micro|0.584|0.925|0.91|0.929|
|eval_runtime|50.84|0.65|0.123|0.51|
|eval_samples_per_second|167.193|1455.491|1531.96|1484.664|
|eval_steps_per_second|2.616|23.079|24.317|23.535|
|Size of dataset|8500|946|189|757|

### Framework versions

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