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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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- fin |
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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: fin1 |
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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: fin |
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type: fin |
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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: 0.8315412186379928 |
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- name: Recall |
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type: recall |
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value: 0.9243027888446215 |
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- name: F1 |
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type: f1 |
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value: 0.8754716981132076 |
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- name: Accuracy |
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type: accuracy |
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value: 0.985175455057234 |
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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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# fin1 |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the fin dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0778 |
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- Precision: 0.8315 |
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- Recall: 0.9243 |
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- F1: 0.8755 |
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- Accuracy: 0.9852 |
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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: 5 |
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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 | 129 | 0.0860 | 0.8535 | 0.9283 | 0.8893 | 0.9904 | |
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| No log | 2.0 | 258 | 0.1513 | 0.7993 | 0.9203 | 0.8556 | 0.9799 | |
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| No log | 3.0 | 387 | 0.0977 | 0.8221 | 0.9203 | 0.8684 | 0.9831 | |
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| 0.0017 | 4.0 | 516 | 0.0783 | 0.8286 | 0.9243 | 0.8738 | 0.9848 | |
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| 0.0017 | 5.0 | 645 | 0.0778 | 0.8315 | 0.9243 | 0.8755 | 0.9852 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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