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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- movie_releases |
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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: bert-finetuned-radarr |
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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: movie_releases |
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type: movie_releases |
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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: 0.9555421444377389 |
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- name: Recall |
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type: recall |
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value: 0.9638798701298701 |
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- name: F1 |
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type: f1 |
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value: 0.9596928982725529 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9817602584524263 |
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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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# bert-finetuned-radarr |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the movie_releases dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0731 |
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- Precision: 0.9555 |
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- Recall: 0.9639 |
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- F1: 0.9597 |
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- Accuracy: 0.9818 |
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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: 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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- num_epochs: 3 |
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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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| 0.0431 | 1.0 | 1191 | 0.1403 | 0.9436 | 0.9574 | 0.9504 | 0.9626 | |
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| 0.0236 | 2.0 | 2382 | 0.0881 | 0.9485 | 0.9560 | 0.9522 | 0.9694 | |
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| 0.0138 | 3.0 | 3573 | 0.0731 | 0.9555 | 0.9639 | 0.9597 | 0.9818 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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