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

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: 0.3689
- F1 Macro: 0.8721
- F1 Micro: 0.8729
- Accuracy Balanced: 0.8725
- Accuracy: 0.8729
- Precision Macro: 0.8718
- Recall Macro: 0.8725
- Precision Micro: 0.8729
- Recall Micro: 0.8729

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.4942        | 0.17  | 200  | 0.4413          | 0.8081   | 0.8089   | 0.8093            | 0.8089   | 0.8076          | 0.8093       | 0.8089          | 0.8089       |
| 0.4114        | 0.34  | 400  | 0.3991          | 0.8227   | 0.8232   | 0.8250            | 0.8232   | 0.8226          | 0.8250       | 0.8232          | 0.8232       |
| 0.3467        | 0.51  | 600  | 0.3584          | 0.8388   | 0.8391   | 0.8421            | 0.8391   | 0.8396          | 0.8421       | 0.8391          | 0.8391       |
| 0.3402        | 0.68  | 800  | 0.3620          | 0.8534   | 0.8544   | 0.8536            | 0.8544   | 0.8532          | 0.8536       | 0.8544          | 0.8544       |
| 0.3304        | 0.85  | 1000 | 0.3385          | 0.8566   | 0.8576   | 0.8567            | 0.8576   | 0.8565          | 0.8567       | 0.8576          | 0.8576       |
| 0.3234        | 1.02  | 1200 | 0.3456          | 0.8637   | 0.8650   | 0.8631            | 0.8650   | 0.8645          | 0.8631       | 0.8650          | 0.8650       |
| 0.2702        | 1.19  | 1400 | 0.3201          | 0.8606   | 0.8613   | 0.8616            | 0.8613   | 0.8600          | 0.8616       | 0.8613          | 0.8613       |
| 0.2581        | 1.36  | 1600 | 0.3233          | 0.8619   | 0.8624   | 0.8639            | 0.8624   | 0.8615          | 0.8639       | 0.8624          | 0.8624       |
| 0.2414        | 1.52  | 1800 | 0.3451          | 0.8674   | 0.8687   | 0.8664            | 0.8687   | 0.8687          | 0.8664       | 0.8687          | 0.8687       |
| 0.2687        | 1.69  | 2000 | 0.3415          | 0.8577   | 0.8608   | 0.8544            | 0.8608   | 0.8677          | 0.8544       | 0.8608          | 0.8608       |
| 0.2518        | 1.86  | 2200 | 0.3378          | 0.8684   | 0.8692   | 0.8688            | 0.8692   | 0.8681          | 0.8688       | 0.8692          | 0.8692       |
| 0.2182        | 2.03  | 2400 | 0.3581          | 0.8698   | 0.8708   | 0.8697            | 0.8708   | 0.8700          | 0.8697       | 0.8708          | 0.8708       |
| 0.1919        | 2.2   | 2600 | 0.3671          | 0.8677   | 0.8687   | 0.8676            | 0.8687   | 0.8678          | 0.8676       | 0.8687          | 0.8687       |
| 0.1771        | 2.37  | 2800 | 0.3790          | 0.8709   | 0.8719   | 0.8707            | 0.8719   | 0.8710          | 0.8707       | 0.8719          | 0.8719       |
| 0.1793        | 2.54  | 3000 | 0.3856          | 0.8687   | 0.8692   | 0.8701            | 0.8692   | 0.8680          | 0.8701       | 0.8692          | 0.8692       |
| 0.1909        | 2.71  | 3200 | 0.3777          | 0.8686   | 0.8698   | 0.8682            | 0.8698   | 0.8691          | 0.8682       | 0.8698          | 0.8698       |
| 0.2021        | 2.88  | 3400 | 0.3685          | 0.8701   | 0.8708   | 0.8710            | 0.8708   | 0.8696          | 0.8710       | 0.8708          | 0.8708       |

### eval result
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.355|0.246|0.369|0.337|
|eval_f1_macro|0.872|0.932|0.872|0.88|
|eval_f1_micro|0.873|0.932|0.873|0.881|
|eval_accuracy_balanced|0.872|0.932|0.873|0.88|
|eval_accuracy|0.873|0.932|0.873|0.881|
|eval_precision_macro|0.873|0.932|0.872|0.881|
|eval_recall_macro|0.872|0.932|0.873|0.88|
|eval_precision_micro|0.873|0.932|0.873|0.881|
|eval_recall_micro|0.873|0.932|0.873|0.881|
|eval_runtime|50.724|0.611|11.126|44.342|
|eval_samples_per_second|167.574|1547.575|169.783|170.424|
|eval_steps_per_second|2.622|24.539|2.696|2.684|
|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