---
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