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update model card README.md
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README.md
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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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