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---
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
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-nli-2mil7
This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3496
- F1 Macro: 0.8808
- F1 Micro: 0.8813
- Accuracy Balanced: 0.8806
- Accuracy: 0.8813
- Precision Macro: 0.8810
- Recall Macro: 0.8806
- Precision Micro: 0.8813
- Recall Micro: 0.8813
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 20241201
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.4669 | 0.17 | 200 | 0.4194 | 0.8011 | 0.8015 | 0.8068 | 0.8015 | 0.8029 | 0.8068 | 0.8015 | 0.8015 |
| 0.3921 | 0.34 | 400 | 0.4010 | 0.8139 | 0.8205 | 0.8095 | 0.8205 | 0.8283 | 0.8095 | 0.8205 | 0.8205 |
| 0.3468 | 0.51 | 600 | 0.3457 | 0.8459 | 0.8486 | 0.8445 | 0.8486 | 0.8478 | 0.8445 | 0.8486 | 0.8486 |
| 0.3299 | 0.68 | 800 | 0.3523 | 0.8595 | 0.8613 | 0.8598 | 0.8613 | 0.8593 | 0.8598 | 0.8613 | 0.8613 |
| 0.3192 | 0.85 | 1000 | 0.3372 | 0.8570 | 0.8592 | 0.8563 | 0.8592 | 0.8578 | 0.8563 | 0.8592 | 0.8592 |
| 0.3063 | 1.02 | 1200 | 0.3502 | 0.8594 | 0.8602 | 0.8627 | 0.8602 | 0.8585 | 0.8627 | 0.8602 | 0.8602 |
| 0.2481 | 1.19 | 1400 | 0.3579 | 0.8600 | 0.8624 | 0.8589 | 0.8624 | 0.8615 | 0.8589 | 0.8624 | 0.8624 |
| 0.2447 | 1.35 | 1600 | 0.3617 | 0.8636 | 0.8650 | 0.8649 | 0.8650 | 0.8628 | 0.8649 | 0.8650 | 0.8650 |
| 0.2496 | 1.52 | 1800 | 0.3494 | 0.8658 | 0.8677 | 0.8654 | 0.8677 | 0.8661 | 0.8654 | 0.8677 | 0.8677 |
| 0.2444 | 1.69 | 2000 | 0.3345 | 0.8644 | 0.8666 | 0.8635 | 0.8666 | 0.8656 | 0.8635 | 0.8666 | 0.8666 |
| 0.2217 | 1.86 | 2200 | 0.3452 | 0.8714 | 0.8724 | 0.8737 | 0.8724 | 0.8703 | 0.8737 | 0.8724 | 0.8724 |
| 0.2149 | 2.03 | 2400 | 0.3673 | 0.8727 | 0.8740 | 0.8737 | 0.8740 | 0.8719 | 0.8737 | 0.8740 | 0.8740 |
| 0.166 | 2.2 | 2600 | 0.3971 | 0.8731 | 0.8751 | 0.8723 | 0.8751 | 0.8741 | 0.8723 | 0.8751 | 0.8751 |
| 0.1685 | 2.37 | 2800 | 0.3884 | 0.8696 | 0.8714 | 0.8693 | 0.8714 | 0.8698 | 0.8693 | 0.8714 | 0.8714 |
| 0.1737 | 2.54 | 3000 | 0.3896 | 0.8674 | 0.8692 | 0.8672 | 0.8692 | 0.8676 | 0.8672 | 0.8692 | 0.8692 |
| 0.1667 | 2.71 | 3200 | 0.3950 | 0.8718 | 0.8735 | 0.8717 | 0.8735 | 0.8718 | 0.8717 | 0.8735 | 0.8735 |
| 0.1811 | 2.88 | 3400 | 0.3889 | 0.8707 | 0.8724 | 0.8708 | 0.8724 | 0.8707 | 0.8708 | 0.8724 | 0.8724 |
### Eval result
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.365|0.29|0.389|0.35|
|eval_f1_macro|0.875|0.911|0.87|0.881|
|eval_f1_micro|0.876|0.911|0.871|0.881|
|eval_accuracy_balanced|0.875|0.911|0.87|0.881|
|eval_accuracy|0.876|0.911|0.871|0.881|
|eval_precision_macro|0.875|0.912|0.87|0.881|
|eval_recall_macro|0.875|0.911|0.87|0.881|
|eval_precision_micro|0.876|0.911|0.871|0.881|
|eval_recall_micro|0.876|0.911|0.871|0.881|
|eval_runtime|232.017|4.063|51.192|204.15|
|eval_samples_per_second|36.635|232.844|36.9|37.017|
|eval_steps_per_second|0.289|1.969|0.293|0.294|
|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 |