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--- |
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base_model: asadfgglie/mDeBERTa-v3-base-xnli-multilingual-zeroshot-v1.0 |
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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-zeroshot-v1.1-seed20241201 |
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results: [] |
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datasets: |
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- asadfgglie/nli-zh-tw-all |
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- asadfgglie/BanBan_2024-10-17-facial_expressions-nli |
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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-v1.1-seed20241201 |
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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`. |
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Original version use `RANDOM_SEED=42`, this version use `RANDOM_SEED=20241201`. |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6134 |
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- F1 Macro: 0.8616 |
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- F1 Micro: 0.8634 |
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- Accuracy Balanced: 0.8616 |
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- Accuracy: 0.8634 |
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- Precision Macro: 0.8616 |
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- Recall Macro: 0.8616 |
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- Precision Micro: 0.8634 |
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- Recall Micro: 0.8634 |
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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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| 0.2034 | 0.17 | 200 | 0.4241 | 0.8481 | 0.8518 | 0.8451 | 0.8518 | 0.8541 | 0.8451 | 0.8518 | 0.8518 | |
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| 0.219 | 0.34 | 400 | 0.4178 | 0.8608 | 0.8624 | 0.8615 | 0.8624 | 0.8602 | 0.8615 | 0.8624 | 0.8624 | |
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| 0.2142 | 0.51 | 600 | 0.3810 | 0.8572 | 0.8602 | 0.8548 | 0.8602 | 0.8613 | 0.8548 | 0.8602 | 0.8602 | |
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| 0.199 | 0.68 | 800 | 0.4314 | 0.8537 | 0.8571 | 0.8508 | 0.8571 | 0.8590 | 0.8508 | 0.8571 | 0.8571 | |
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| 0.2005 | 0.85 | 1000 | 0.4282 | 0.8572 | 0.8602 | 0.8547 | 0.8602 | 0.8615 | 0.8547 | 0.8602 | 0.8602 | |
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| 0.1846 | 1.02 | 1200 | 0.4631 | 0.8691 | 0.8703 | 0.8707 | 0.8703 | 0.8681 | 0.8707 | 0.8703 | 0.8703 | |
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| 0.154 | 1.19 | 1400 | 0.4922 | 0.8599 | 0.8613 | 0.8610 | 0.8613 | 0.8590 | 0.8610 | 0.8613 | 0.8613 | |
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| 0.1432 | 1.35 | 1600 | 0.5020 | 0.8540 | 0.8560 | 0.8540 | 0.8560 | 0.8541 | 0.8540 | 0.8560 | 0.8560 | |
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| 0.1335 | 1.52 | 1800 | 0.5313 | 0.8479 | 0.8507 | 0.8461 | 0.8507 | 0.8505 | 0.8461 | 0.8507 | 0.8507 | |
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| 0.1373 | 1.69 | 2000 | 0.5018 | 0.8546 | 0.8571 | 0.8533 | 0.8571 | 0.8563 | 0.8533 | 0.8571 | 0.8571 | |
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| 0.128 | 1.86 | 2200 | 0.4896 | 0.8644 | 0.8655 | 0.8665 | 0.8655 | 0.8633 | 0.8665 | 0.8655 | 0.8655 | |
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| 0.1257 | 2.03 | 2400 | 0.4922 | 0.8648 | 0.8666 | 0.8648 | 0.8666 | 0.8648 | 0.8648 | 0.8666 | 0.8666 | |
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| 0.0959 | 2.2 | 2600 | 0.5814 | 0.8589 | 0.8613 | 0.8576 | 0.8613 | 0.8606 | 0.8576 | 0.8613 | 0.8613 | |
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| 0.0918 | 2.37 | 2800 | 0.5987 | 0.8617 | 0.8634 | 0.8618 | 0.8634 | 0.8615 | 0.8618 | 0.8634 | 0.8634 | |
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| 0.0992 | 2.54 | 3000 | 0.6117 | 0.8631 | 0.8650 | 0.8629 | 0.8650 | 0.8634 | 0.8629 | 0.8650 | 0.8650 | |
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| 0.0897 | 2.71 | 3200 | 0.6191 | 0.8583 | 0.8602 | 0.8583 | 0.8602 | 0.8584 | 0.8583 | 0.8602 | 0.8602 | |
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| 0.1065 | 2.88 | 3400 | 0.6221 | 0.8625 | 0.8645 | 0.8619 | 0.8645 | 0.8631 | 0.8619 | 0.8645 | 0.8645 | |
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### Eval result |
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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|0.575|0.356|0.613|0.538| |
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|eval_f1_macro|0.87|0.896|0.862|0.875| |
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|eval_f1_micro|0.871|0.896|0.863|0.876| |
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|eval_accuracy_balanced|0.869|0.896|0.862|0.875| |
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|eval_accuracy|0.871|0.896|0.863|0.876| |
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|eval_precision_macro|0.871|0.898|0.862|0.877| |
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|eval_recall_macro|0.869|0.896|0.862|0.875| |
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|eval_precision_micro|0.871|0.896|0.863|0.876| |
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|eval_recall_micro|0.871|0.896|0.863|0.876| |
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|eval_runtime|229.732|4.248|51.374|204.44| |
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|eval_samples_per_second|37.0|222.68|36.769|36.964| |
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|eval_steps_per_second|0.292|1.883|0.292|0.293| |
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|Size of dataset|8500|946|1889|7557| |
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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 |
