|
--- |
|
license: mit |
|
base_model: joeddav/xlm-roberta-large-xnli |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: xlm-roberta-large-xnli-v4.0 |
|
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-v4.0 |
|
|
|
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: 0.4963 |
|
- F1 Macro: 0.8192 |
|
- F1 Micro: 0.8204 |
|
- Accuracy Balanced: 0.8190 |
|
- Accuracy: 0.8204 |
|
- Precision Macro: 0.8193 |
|
- Recall Macro: 0.8190 |
|
- Precision Micro: 0.8204 |
|
- Recall Micro: 0.8204 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
|
| 0.3593 | 1.69 | 200 | 0.4297 | 0.8211 | 0.8218 | 0.8224 | 0.8218 | 0.8206 | 0.8224 | 0.8218 | 0.8218 | |
|
|
|
### eval result |
|
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset| |
|
| :---: | :---: | :---: | :---: | :---: | |
|
|eval_loss|0.494|0.773|0.483|0.496| |
|
|eval_f1_macro|0.821|0.627|0.825|0.819| |
|
|eval_f1_micro|0.822|0.644|0.826|0.82| |
|
|eval_accuracy_balanced|0.821|0.638|0.826|0.819| |
|
|eval_accuracy|0.822|0.644|0.826|0.82| |
|
|eval_precision_macro|0.821|0.663|0.825|0.819| |
|
|eval_recall_macro|0.821|0.638|0.826|0.819| |
|
|eval_precision_micro|0.822|0.644|0.826|0.82| |
|
|eval_recall_micro|0.822|0.644|0.826|0.82| |
|
|eval_runtime|50.82|0.635|10.346|39.781| |
|
|eval_samples_per_second|167.257|1490.523|164.308|170.938| |
|
|eval_steps_per_second|2.617|23.634|2.61|2.69| |
|
|Size of dataset|8500|946|1700|6800| |
|
|
|
### Framework versions |
|
|
|
- Transformers 4.33.3 |
|
- Pytorch 2.5.1+cu121 |
|
- Datasets 2.14.7 |
|
- Tokenizers 0.13.3 |
|
|
