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---
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 |