metadata
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
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, but training set was downsampled as 80% size of non-nli dataset asadfgglie/BanBan_2024-10-17-facial_expressions-nli.
This model is a fine-tuned version of 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