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--- |
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base_model: Fsoft-AIC/videberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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model-index: |
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- name: mrc-videnerta-dsc |
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results: [] |
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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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# mrc-videnerta-dsc |
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This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8100 |
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- Exact Match: 0.5970 |
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- F1: 0.7653 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:-----------:|:------:| |
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| 0.967 | 1.0 | 3098 | 0.8918 | 0.5358 | 0.7128 | |
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| 0.8396 | 2.0 | 6196 | 0.8346 | 0.5610 | 0.7488 | |
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| 0.7209 | 3.0 | 9294 | 0.8057 | 0.5825 | 0.7624 | |
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| 0.6532 | 4.0 | 12392 | 0.8212 | 0.5951 | 0.7513 | |
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| 0.601 | 5.0 | 15490 | 0.8100 | 0.5970 | 0.7653 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.2.1 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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