---
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-seed20241201
  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-seed20241201

This model use same hyper-parameter with [asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1](https://huggingface.co/asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.1), except `RANDOM_SEED`.

Original version use `RANDOM_SEED=42`, this version use `RANDOM_SEED=20241201`.

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: 0.6134
- F1 Macro: 0.8616
- F1 Micro: 0.8634
- Accuracy Balanced: 0.8616
- Accuracy: 0.8634
- Precision Macro: 0.8616
- Recall Macro: 0.8616
- Precision Micro: 0.8634
- Recall Micro: 0.8634

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.2034        | 0.17  | 200  | 0.4241          | 0.8481   | 0.8518   | 0.8451            | 0.8518   | 0.8541          | 0.8451       | 0.8518          | 0.8518       |
| 0.219         | 0.34  | 400  | 0.4178          | 0.8608   | 0.8624   | 0.8615            | 0.8624   | 0.8602          | 0.8615       | 0.8624          | 0.8624       |
| 0.2142        | 0.51  | 600  | 0.3810          | 0.8572   | 0.8602   | 0.8548            | 0.8602   | 0.8613          | 0.8548       | 0.8602          | 0.8602       |
| 0.199         | 0.68  | 800  | 0.4314          | 0.8537   | 0.8571   | 0.8508            | 0.8571   | 0.8590          | 0.8508       | 0.8571          | 0.8571       |
| 0.2005        | 0.85  | 1000 | 0.4282          | 0.8572   | 0.8602   | 0.8547            | 0.8602   | 0.8615          | 0.8547       | 0.8602          | 0.8602       |
| 0.1846        | 1.02  | 1200 | 0.4631          | 0.8691   | 0.8703   | 0.8707            | 0.8703   | 0.8681          | 0.8707       | 0.8703          | 0.8703       |
| 0.154         | 1.19  | 1400 | 0.4922          | 0.8599   | 0.8613   | 0.8610            | 0.8613   | 0.8590          | 0.8610       | 0.8613          | 0.8613       |
| 0.1432        | 1.35  | 1600 | 0.5020          | 0.8540   | 0.8560   | 0.8540            | 0.8560   | 0.8541          | 0.8540       | 0.8560          | 0.8560       |
| 0.1335        | 1.52  | 1800 | 0.5313          | 0.8479   | 0.8507   | 0.8461            | 0.8507   | 0.8505          | 0.8461       | 0.8507          | 0.8507       |
| 0.1373        | 1.69  | 2000 | 0.5018          | 0.8546   | 0.8571   | 0.8533            | 0.8571   | 0.8563          | 0.8533       | 0.8571          | 0.8571       |
| 0.128         | 1.86  | 2200 | 0.4896          | 0.8644   | 0.8655   | 0.8665            | 0.8655   | 0.8633          | 0.8665       | 0.8655          | 0.8655       |
| 0.1257        | 2.03  | 2400 | 0.4922          | 0.8648   | 0.8666   | 0.8648            | 0.8666   | 0.8648          | 0.8648       | 0.8666          | 0.8666       |
| 0.0959        | 2.2   | 2600 | 0.5814          | 0.8589   | 0.8613   | 0.8576            | 0.8613   | 0.8606          | 0.8576       | 0.8613          | 0.8613       |
| 0.0918        | 2.37  | 2800 | 0.5987          | 0.8617   | 0.8634   | 0.8618            | 0.8634   | 0.8615          | 0.8618       | 0.8634          | 0.8634       |
| 0.0992        | 2.54  | 3000 | 0.6117          | 0.8631   | 0.8650   | 0.8629            | 0.8650   | 0.8634          | 0.8629       | 0.8650          | 0.8650       |
| 0.0897        | 2.71  | 3200 | 0.6191          | 0.8583   | 0.8602   | 0.8583            | 0.8602   | 0.8584          | 0.8583       | 0.8602          | 0.8602       |
| 0.1065        | 2.88  | 3400 | 0.6221          | 0.8625   | 0.8645   | 0.8619            | 0.8645   | 0.8631          | 0.8619       | 0.8645          | 0.8645       |


### Eval result

|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.575|0.356|0.613|0.538|
|eval_f1_macro|0.87|0.896|0.862|0.875|
|eval_f1_micro|0.871|0.896|0.863|0.876|
|eval_accuracy_balanced|0.869|0.896|0.862|0.875|
|eval_accuracy|0.871|0.896|0.863|0.876|
|eval_precision_macro|0.871|0.898|0.862|0.877|
|eval_recall_macro|0.869|0.896|0.862|0.875|
|eval_precision_micro|0.871|0.896|0.863|0.876|
|eval_recall_micro|0.871|0.896|0.863|0.876|
|eval_runtime|229.732|4.248|51.374|204.44|
|eval_samples_per_second|37.0|222.68|36.769|36.964|
|eval_steps_per_second|0.292|1.883|0.292|0.293|
|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