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-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
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 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