|
--- |
|
base_model: asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1 |
|
results: [] |
|
datasets: |
|
- asadfgglie/nli-zh-tw-all |
|
- asadfgglie/BanBan_2024-10-17-facial_expressions-nli |
|
language: |
|
- zh |
|
pipeline_tag: zero-shot-classification |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1 |
|
|
|
This model is a fine-tuned version of [asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0](https://huggingface.co/asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5335 |
|
- F1 Macro: 0.8675 |
|
- F1 Micro: 0.8692 |
|
- Accuracy Balanced: 0.8674 |
|
- Accuracy: 0.8692 |
|
- Precision Macro: 0.8677 |
|
- Recall Macro: 0.8674 |
|
- Precision Micro: 0.8692 |
|
- Recall Micro: 0.8692 |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 2e-05 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 128 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
|
| 0.1975 | 0.17 | 200 | 0.3474 | 0.8688 | 0.8708 | 0.8678 | 0.8708 | 0.8701 | 0.8678 | 0.8708 | 0.8708 | |
|
| 0.1974 | 0.34 | 400 | 0.3580 | 0.8600 | 0.8624 | 0.8585 | 0.8624 | 0.8621 | 0.8585 | 0.8624 | 0.8624 | |
|
| 0.2054 | 0.51 | 600 | 0.3616 | 0.8520 | 0.8565 | 0.8476 | 0.8565 | 0.8638 | 0.8476 | 0.8565 | 0.8565 | |
|
| 0.2094 | 0.68 | 800 | 0.3772 | 0.8658 | 0.8687 | 0.8630 | 0.8687 | 0.8710 | 0.8630 | 0.8687 | 0.8687 | |
|
| 0.2118 | 0.85 | 1000 | 0.3701 | 0.8729 | 0.8740 | 0.8747 | 0.8740 | 0.8719 | 0.8747 | 0.8740 | 0.8740 | |
|
| 0.1948 | 1.02 | 1200 | 0.3778 | 0.8698 | 0.8714 | 0.8702 | 0.8714 | 0.8696 | 0.8702 | 0.8714 | 0.8714 | |
|
| 0.1447 | 1.19 | 1400 | 0.3964 | 0.8666 | 0.8692 | 0.8642 | 0.8692 | 0.8706 | 0.8642 | 0.8692 | 0.8692 | |
|
| 0.1723 | 1.35 | 1600 | 0.3855 | 0.8718 | 0.8735 | 0.8716 | 0.8735 | 0.8720 | 0.8716 | 0.8735 | 0.8735 | |
|
| 0.1476 | 1.52 | 1800 | 0.4164 | 0.8637 | 0.8661 | 0.8620 | 0.8661 | 0.8661 | 0.8620 | 0.8661 | 0.8661 | |
|
| 0.1515 | 1.69 | 2000 | 0.3958 | 0.8724 | 0.8740 | 0.8725 | 0.8740 | 0.8724 | 0.8725 | 0.8740 | 0.8740 | |
|
| 0.1378 | 1.86 | 2200 | 0.4390 | 0.8694 | 0.8708 | 0.8699 | 0.8708 | 0.8689 | 0.8699 | 0.8708 | 0.8708 | |
|
| 0.1332 | 2.03 | 2400 | 0.4535 | 0.8732 | 0.8745 | 0.8740 | 0.8745 | 0.8726 | 0.8740 | 0.8745 | 0.8745 | |
|
| 0.0913 | 2.2 | 2600 | 0.5235 | 0.8638 | 0.8661 | 0.8625 | 0.8661 | 0.8656 | 0.8625 | 0.8661 | 0.8661 | |
|
| 0.1076 | 2.37 | 2800 | 0.5339 | 0.8638 | 0.8661 | 0.8623 | 0.8661 | 0.8659 | 0.8623 | 0.8661 | 0.8661 | |
|
| 0.09 | 2.54 | 3000 | 0.5388 | 0.8670 | 0.8687 | 0.8667 | 0.8687 | 0.8672 | 0.8667 | 0.8687 | 0.8687 | |
|
| 0.0928 | 2.71 | 3200 | 0.5266 | 0.8649 | 0.8666 | 0.8648 | 0.8666 | 0.8650 | 0.8648 | 0.8666 | 0.8666 | |
|
| 0.0805 | 2.88 | 3400 | 0.5433 | 0.8658 | 0.8677 | 0.8654 | 0.8677 | 0.8663 | 0.8654 | 0.8677 | 0.8677 | |
|
|
|
### Eval results |
|
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset| |
|
| :---: | :---: | :---: | :---: | :---: | |
|
|eval_loss|0.576|0.165|0.584|0.523| |
|
|eval_f1_macro|0.869|0.945|0.868|0.878| |
|
|eval_f1_micro|0.87|0.945|0.87|0.879| |
|
|eval_accuracy_balanced|0.868|0.945|0.867|0.878| |
|
|eval_accuracy|0.87|0.945|0.87|0.879| |
|
|eval_precision_macro|0.87|0.945|0.868|0.88| |
|
|eval_recall_macro|0.868|0.945|0.867|0.878| |
|
|eval_precision_micro|0.87|0.945|0.87|0.879| |
|
|eval_recall_micro|0.87|0.945|0.87|0.879| |
|
|eval_runtime|229.83|4.05|51.2|203.627| |
|
|eval_samples_per_second|36.984|233.57|36.894|37.112| |
|
|eval_steps_per_second|0.292|1.975|0.293|0.295| |
|
|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 |
