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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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- favsbot |
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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-base-cased-NER-favsbot |
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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: favsbot |
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type: favsbot |
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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: 0.8571428571428571 |
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- name: Recall |
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type: recall |
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value: 0.96 |
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- name: F1 |
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type: f1 |
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value: 0.9056603773584904 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9583333333333334 |
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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-base-cased-NER-favsbot |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0992 |
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- Precision: 0.8571 |
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- Recall: 0.96 |
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- F1: 0.9057 |
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- Accuracy: 0.9583 |
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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: 1.5e-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: 20 |
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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 | 1.0 | 10 | 1.7643 | 0.0 | 0.0 | 0.0 | 0.5694 | |
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| No log | 2.0 | 20 | 1.1420 | 0.0 | 0.0 | 0.0 | 0.5833 | |
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| No log | 3.0 | 30 | 0.7946 | 0.9375 | 0.6 | 0.7317 | 0.8056 | |
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| No log | 4.0 | 40 | 0.5625 | 0.8182 | 0.72 | 0.7660 | 0.8611 | |
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| No log | 5.0 | 50 | 0.4217 | 0.8148 | 0.88 | 0.8462 | 0.9306 | |
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| No log | 6.0 | 60 | 0.3082 | 0.8519 | 0.92 | 0.8846 | 0.9444 | |
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| No log | 7.0 | 70 | 0.2386 | 0.8148 | 0.88 | 0.8462 | 0.9444 | |
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| No log | 8.0 | 80 | 0.1965 | 0.8148 | 0.88 | 0.8462 | 0.9444 | |
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| No log | 9.0 | 90 | 0.1626 | 0.8148 | 0.88 | 0.8462 | 0.9444 | |
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| No log | 10.0 | 100 | 0.1465 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 11.0 | 110 | 0.1314 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 12.0 | 120 | 0.1215 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 13.0 | 130 | 0.1160 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 14.0 | 140 | 0.1104 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 15.0 | 150 | 0.1050 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 16.0 | 160 | 0.1012 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 17.0 | 170 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 18.0 | 180 | 0.0997 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 19.0 | 190 | 0.0995 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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| No log | 20.0 | 200 | 0.0992 | 0.8571 | 0.96 | 0.9057 | 0.9583 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.6.1 |
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- Tokenizers 0.12.1 |
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