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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: slurp-slot_baseline-xlm_r-en |
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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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# slurp-slot_baseline-xlm_r-en |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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: 0.3263 |
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- Precision: 0.8145 |
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- Recall: 0.8641 |
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- F1: 0.8386 |
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- Accuracy: 0.9341 |
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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: 16 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 1.1437 | 1.0 | 720 | 0.5236 | 0.6852 | 0.6623 | 0.6736 | 0.8860 | |
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| 0.5761 | 2.0 | 1440 | 0.3668 | 0.7348 | 0.7829 | 0.7581 | 0.9119 | |
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| 0.3087 | 3.0 | 2160 | 0.2996 | 0.7925 | 0.8280 | 0.8099 | 0.9270 | |
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| 0.2631 | 4.0 | 2880 | 0.2959 | 0.7872 | 0.8487 | 0.8168 | 0.9275 | |
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| 0.1847 | 5.0 | 3600 | 0.3121 | 0.7929 | 0.8373 | 0.8145 | 0.9290 | |
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| 0.1518 | 6.0 | 4320 | 0.3117 | 0.8080 | 0.8601 | 0.8332 | 0.9329 | |
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| 0.1232 | 7.0 | 5040 | 0.3153 | 0.7961 | 0.8490 | 0.8217 | 0.9267 | |
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| 0.0994 | 8.0 | 5760 | 0.3125 | 0.8105 | 0.8570 | 0.8331 | 0.9332 | |
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| 0.0968 | 9.0 | 6480 | 0.3242 | 0.8147 | 0.8637 | 0.8385 | 0.9329 | |
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| 0.0772 | 10.0 | 7200 | 0.3263 | 0.8145 | 0.8641 | 0.8386 | 0.9341 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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