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--- |
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license: mit |
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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: fine-tuned-QAS-Squad_2-with-xlm-roberta-large |
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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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# fine-tuned-QAS-Squad_2-with-xlm-roberta-large |
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8615 |
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- Exact Match: 69.2340 |
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- F1: 82.5542 |
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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: 1e-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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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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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: 10 |
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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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| 1.0967 | 0.5 | 464 | 1.0076 | 57.8908 | 71.8971 | |
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| 0.912 | 1.0 | 928 | 0.8118 | 65.0306 | 79.0193 | |
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| 0.8071 | 1.5 | 1392 | 0.7587 | 67.2288 | 80.3986 | |
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| 0.7414 | 2.0 | 1856 | 0.7322 | 68.3614 | 81.3949 | |
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| 0.6548 | 2.5 | 2320 | 0.7685 | 67.5896 | 81.3012 | |
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| 0.624 | 3.0 | 2784 | 0.7307 | 68.5544 | 82.0875 | |
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| 0.5412 | 3.5 | 3248 | 0.7606 | 69.2340 | 82.4384 | |
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| 0.5356 | 4.0 | 3712 | 0.7352 | 69.5612 | 82.7509 | |
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| 0.4463 | 4.5 | 4176 | 0.7862 | 69.2843 | 82.3298 | |
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| 0.4899 | 5.0 | 4640 | 0.7868 | 69.5109 | 82.7397 | |
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| 0.417 | 5.5 | 5104 | 0.8615 | 69.2340 | 82.5542 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.2.0 |
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- Tokenizers 0.13.2 |
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