---
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mDeBERTa-v3-base-xnli-multilingual-zeroshot-v5.0-nli-downsample-and-non-nli
  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
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# mDeBERTa-v3-base-xnli-multilingual-zeroshot-v5.0-nli-downsample-and-non-nli

This model is merge dataset stratege version of v3.0 and v4.0.

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: 0.4531
- F1 Macro: 0.8330
- F1 Micro: 0.8337
- Accuracy Balanced: 0.8331
- Accuracy: 0.8337
- Precision Macro: 0.8330
- Recall Macro: 0.8331
- Precision Micro: 0.8337
- Recall Micro: 0.8337

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.3748        | 0.85  | 200  | 0.4218          | 0.7971   | 0.7999   | 0.7970            | 0.7999   | 0.7973          | 0.7970       | 0.7999          | 0.7999       |
| 0.2693        | 1.69  | 400  | 0.4523          | 0.8061   | 0.8078   | 0.8077            | 0.8078   | 0.8053          | 0.8077       | 0.8078          | 0.8078       |
| 0.1905        | 2.54  | 600  | 0.4720          | 0.8226   | 0.8242   | 0.8241            | 0.8242   | 0.8217          | 0.8241       | 0.8242          | 0.8242       |

### Eval results

|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.48|0.269|0.484|0.453|
|eval_f1_macro|0.821|0.909|0.816|0.833|
|eval_f1_micro|0.822|0.909|0.818|0.834|
|eval_accuracy_balanced|0.821|0.909|0.816|0.833|
|eval_accuracy|0.822|0.909|0.818|0.834|
|eval_precision_macro|0.821|0.909|0.816|0.833|
|eval_recall_macro|0.821|0.909|0.816|0.833|
|eval_precision_micro|0.822|0.909|0.818|0.834|
|eval_recall_micro|0.822|0.909|0.818|0.834|
|eval_runtime|239.87|4.066|58.954|236.797|
|eval_samples_per_second|35.436|232.633|32.042|31.913|
|eval_steps_per_second|0.279|1.967|0.254|0.253|
|epoch|2.99|2.99|2.99|2.99|
|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