---
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
  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

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: 0.5335
- F1 Macro: 0.8675
- F1 Micro: 0.8692
- Accuracy Balanced: 0.8674
- Accuracy: 0.8692
- Precision Macro: 0.8677
- Recall Macro: 0.8674
- Precision Micro: 0.8692
- Recall Micro: 0.8692

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 128
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.1975        | 0.17  | 200  | 0.3474          | 0.8688   | 0.8708   | 0.8678            | 0.8708   | 0.8701          | 0.8678       | 0.8708          | 0.8708       |
| 0.1974        | 0.34  | 400  | 0.3580          | 0.8600   | 0.8624   | 0.8585            | 0.8624   | 0.8621          | 0.8585       | 0.8624          | 0.8624       |
| 0.2054        | 0.51  | 600  | 0.3616          | 0.8520   | 0.8565   | 0.8476            | 0.8565   | 0.8638          | 0.8476       | 0.8565          | 0.8565       |
| 0.2094        | 0.68  | 800  | 0.3772          | 0.8658   | 0.8687   | 0.8630            | 0.8687   | 0.8710          | 0.8630       | 0.8687          | 0.8687       |
| 0.2118        | 0.85  | 1000 | 0.3701          | 0.8729   | 0.8740   | 0.8747            | 0.8740   | 0.8719          | 0.8747       | 0.8740          | 0.8740       |
| 0.1948        | 1.02  | 1200 | 0.3778          | 0.8698   | 0.8714   | 0.8702            | 0.8714   | 0.8696          | 0.8702       | 0.8714          | 0.8714       |
| 0.1447        | 1.19  | 1400 | 0.3964          | 0.8666   | 0.8692   | 0.8642            | 0.8692   | 0.8706          | 0.8642       | 0.8692          | 0.8692       |
| 0.1723        | 1.35  | 1600 | 0.3855          | 0.8718   | 0.8735   | 0.8716            | 0.8735   | 0.8720          | 0.8716       | 0.8735          | 0.8735       |
| 0.1476        | 1.52  | 1800 | 0.4164          | 0.8637   | 0.8661   | 0.8620            | 0.8661   | 0.8661          | 0.8620       | 0.8661          | 0.8661       |
| 0.1515        | 1.69  | 2000 | 0.3958          | 0.8724   | 0.8740   | 0.8725            | 0.8740   | 0.8724          | 0.8725       | 0.8740          | 0.8740       |
| 0.1378        | 1.86  | 2200 | 0.4390          | 0.8694   | 0.8708   | 0.8699            | 0.8708   | 0.8689          | 0.8699       | 0.8708          | 0.8708       |
| 0.1332        | 2.03  | 2400 | 0.4535          | 0.8732   | 0.8745   | 0.8740            | 0.8745   | 0.8726          | 0.8740       | 0.8745          | 0.8745       |
| 0.0913        | 2.2   | 2600 | 0.5235          | 0.8638   | 0.8661   | 0.8625            | 0.8661   | 0.8656          | 0.8625       | 0.8661          | 0.8661       |
| 0.1076        | 2.37  | 2800 | 0.5339          | 0.8638   | 0.8661   | 0.8623            | 0.8661   | 0.8659          | 0.8623       | 0.8661          | 0.8661       |
| 0.09          | 2.54  | 3000 | 0.5388          | 0.8670   | 0.8687   | 0.8667            | 0.8687   | 0.8672          | 0.8667       | 0.8687          | 0.8687       |
| 0.0928        | 2.71  | 3200 | 0.5266          | 0.8649   | 0.8666   | 0.8648            | 0.8666   | 0.8650          | 0.8648       | 0.8666          | 0.8666       |
| 0.0805        | 2.88  | 3400 | 0.5433          | 0.8658   | 0.8677   | 0.8654            | 0.8677   | 0.8663          | 0.8654       | 0.8677          | 0.8677       |

### Eval results
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.576|0.165|0.584|0.523|
|eval_f1_macro|0.869|0.945|0.868|0.878|
|eval_f1_micro|0.87|0.945|0.87|0.879|
|eval_accuracy_balanced|0.868|0.945|0.867|0.878|
|eval_accuracy|0.87|0.945|0.87|0.879|
|eval_precision_macro|0.87|0.945|0.868|0.88|
|eval_recall_macro|0.868|0.945|0.867|0.878|
|eval_precision_micro|0.87|0.945|0.87|0.879|
|eval_recall_micro|0.87|0.945|0.87|0.879|
|eval_runtime|229.83|4.05|51.2|203.627|
|eval_samples_per_second|36.984|233.57|36.894|37.112|
|eval_steps_per_second|0.292|1.975|0.293|0.295|
|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