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--- |
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library_name: transformers |
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license: mit |
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base_model: FacebookAI/xlm-roberta-base |
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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: test_linsearch_only_abstract |
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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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# test_linsearch_only_abstract |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2716 |
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- Accuracy: 0.6508 |
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- F1 Macro: 0.5942 |
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- Precision Macro: 0.6170 |
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- Recall Macro: 0.5858 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:| |
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| 1.2232 | 1.0 | 2466 | 1.1574 | 0.6405 | 0.5484 | 0.5538 | 0.5608 | |
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| 1.0386 | 2.0 | 4932 | 1.0934 | 0.6497 | 0.5631 | 0.5712 | 0.5642 | |
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| 0.9215 | 3.0 | 7398 | 1.0725 | 0.6634 | 0.5933 | 0.5950 | 0.5970 | |
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| 0.8026 | 4.0 | 9864 | 1.0994 | 0.6532 | 0.5817 | 0.5905 | 0.5796 | |
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| 0.6754 | 5.0 | 12330 | 1.1462 | 0.6558 | 0.5838 | 0.5934 | 0.5806 | |
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| 0.5958 | 6.0 | 14796 | 1.2077 | 0.6537 | 0.5857 | 0.5963 | 0.5813 | |
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| 0.4924 | 7.0 | 17262 | 1.2716 | 0.6508 | 0.5942 | 0.6170 | 0.5858 | |
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| 0.4165 | 8.0 | 19728 | 1.3450 | 0.6450 | 0.5938 | 0.6037 | 0.5923 | |
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| 0.3599 | 9.0 | 22194 | 1.4048 | 0.6412 | 0.5906 | 0.6077 | 0.5812 | |
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| 0.3262 | 10.0 | 24660 | 1.4422 | 0.6389 | 0.5941 | 0.6032 | 0.5894 | |
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### Framework versions |
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- Transformers 4.50.1 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
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