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--- |
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license: mit |
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base_model: roberta-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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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: model |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: generator |
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type: generator |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.5931758530183727 |
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- name: Recall |
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type: recall |
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value: 0.7371167645140247 |
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- name: F1 |
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type: f1 |
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value: 0.6573589296102385 |
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- name: Accuracy |
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type: accuracy |
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value: 0.896675559203776 |
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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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# model |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5350 |
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- Precision: 0.5932 |
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- Recall: 0.7371 |
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- F1: 0.6574 |
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- Accuracy: 0.8967 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 1000 |
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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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| No log | 0.47 | 466 | 0.5513 | 0.5389 | 0.7358 | 0.6222 | 0.8787 | |
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| 0.4041 | 1.47 | 932 | 0.5179 | 0.5398 | 0.7613 | 0.6317 | 0.8797 | |
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| 0.3968 | 2.07 | 1000 | 0.5350 | 0.5932 | 0.7371 | 0.6574 | 0.8967 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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