---
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
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-v4.0-only-nli-downsample

This model use same dataset with [asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0](https://huggingface.co/asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0), but training set was downsampled as 80% size of non-nli dataset [asadfgglie/BanBan_2024-10-17-facial_expressions-nli](https://huggingface.co/datasets/asadfgglie/BanBan_2024-10-17-facial_expressions-nli).

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: 0.4486
- F1 Macro: 0.8264
- F1 Micro: 0.8274
- Accuracy Balanced: 0.8270
- Accuracy: 0.8274
- Precision Macro: 0.8260
- Recall Macro: 0.8270
- Precision Micro: 0.8274
- Recall Micro: 0.8274

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.3242        | 1.69  | 200  | 0.4044          | 0.8308   | 0.8312   | 0.8322            | 0.8312   | 0.8306          | 0.8322       | 0.8312          | 0.8312       |

### Eval results
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.445|1.142|0.429|0.449|
|eval_f1_macro|0.827|0.505|0.83|0.826|
|eval_f1_micro|0.828|0.55|0.831|0.827|
|eval_accuracy_balanced|0.828|0.548|0.831|0.827|
|eval_accuracy|0.828|0.55|0.831|0.827|
|eval_precision_macro|0.827|0.575|0.83|0.826|
|eval_recall_macro|0.828|0.548|0.831|0.827|
|eval_precision_micro|0.828|0.55|0.831|0.827|
|eval_recall_micro|0.828|0.55|0.831|0.827|
|eval_runtime|275.581|4.734|54.573|209.065|
|eval_samples_per_second|30.844|199.853|31.151|32.526|
|eval_steps_per_second|0.243|1.69|0.257|0.258|
|epoch|2.99|2.99|2.99|2.99|
|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