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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: bert-base-NER-finetuned-ner-cerec |
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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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# bert-base-NER-finetuned-ner-cerec |
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This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1511 |
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- Precision: 0.8652 |
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- Recall: 0.8531 |
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- F1: 0.8592 |
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- Accuracy: 0.9790 |
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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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| No log | 1.0 | 45 | 0.4929 | 0.6532 | 0.5664 | 0.6067 | 0.9237 | |
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| No log | 2.0 | 90 | 0.3090 | 0.8145 | 0.7063 | 0.7566 | 0.9497 | |
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| No log | 3.0 | 135 | 0.2477 | 0.8433 | 0.7902 | 0.8159 | 0.9625 | |
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| No log | 4.0 | 180 | 0.2209 | 0.8169 | 0.8112 | 0.8140 | 0.9669 | |
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| No log | 5.0 | 225 | 0.1906 | 0.8369 | 0.8252 | 0.8310 | 0.9739 | |
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| No log | 6.0 | 270 | 0.1568 | 0.8662 | 0.8601 | 0.8632 | 0.9784 | |
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| No log | 7.0 | 315 | 0.1532 | 0.8732 | 0.8671 | 0.8702 | 0.9790 | |
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| No log | 8.0 | 360 | 0.1530 | 0.8671 | 0.8671 | 0.8671 | 0.9790 | |
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| No log | 9.0 | 405 | 0.1526 | 0.8723 | 0.8601 | 0.8662 | 0.9790 | |
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| No log | 10.0 | 450 | 0.1511 | 0.8652 | 0.8531 | 0.8592 | 0.9790 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1 |
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- Datasets 2.10.1 |
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- Tokenizers 0.11.0 |
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