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--- |
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base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 |
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results: [] |
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datasets: |
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- asadfgglie/nli-zh-tw-all |
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language: |
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- zh |
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pipeline_tag: zero-shot-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# mDeBERTa-v3-base-xnli-multilingual-zeroshot-v4.0-only-nli-downsample |
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This model use same dataset with [asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0](https://huggingface.co/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](https://huggingface.co/datasets/asadfgglie/BanBan_2024-10-17-facial_expressions-nli). |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4486 |
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- F1 Macro: 0.8264 |
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- F1 Micro: 0.8274 |
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- Accuracy Balanced: 0.8270 |
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- Accuracy: 0.8274 |
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- Precision Macro: 0.8260 |
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- Recall Macro: 0.8270 |
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- Precision Micro: 0.8274 |
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- Recall Micro: 0.8274 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 128 |
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- seed: 20241201 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.06 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
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| 0.3242 | 1.69 | 200 | 0.4044 | 0.8308 | 0.8312 | 0.8322 | 0.8312 | 0.8306 | 0.8322 | 0.8312 | 0.8312 | |
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### Eval results |
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|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset| |
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| :---: | :---: | :---: | :---: | :---: | |
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|eval_loss|0.445|1.142|0.429|0.449| |
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|eval_f1_macro|0.827|0.505|0.83|0.826| |
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|eval_f1_micro|0.828|0.55|0.831|0.827| |
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|eval_accuracy_balanced|0.828|0.548|0.831|0.827| |
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|eval_accuracy|0.828|0.55|0.831|0.827| |
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|eval_precision_macro|0.827|0.575|0.83|0.826| |
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|eval_recall_macro|0.828|0.548|0.831|0.827| |
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|eval_precision_micro|0.828|0.55|0.831|0.827| |
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|eval_recall_micro|0.828|0.55|0.831|0.827| |
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|eval_runtime|275.581|4.734|54.573|209.065| |
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|eval_samples_per_second|30.844|199.853|31.151|32.526| |
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|eval_steps_per_second|0.243|1.69|0.257|0.258| |
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|epoch|2.99|2.99|2.99|2.99| |
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|Size of dataset|8500|946|1700|6800| |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 2.14.7 |
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- Tokenizers 0.13.3 |
