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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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- NER |
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datasets: |
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- blurb |
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model-index: |
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- name: bert-base-cased-finetuned-ner-BC2GM-IOB |
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results: [] |
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language: |
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- en |
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metrics: |
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- seqeval |
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pipeline_tag: token-classification |
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--- |
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# bert-base-cased-finetuned-ner-BC2GM-IOB |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0813 |
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- Gene |
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- Precision: 0.752111423914654 |
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- Recall: 0.8025296442687747 |
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- F1: 0.7765029830197338 |
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- Number: 6325 |
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- Overall |
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- Precision: 0.7521 |
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- Recall: 0.8025 |
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- F1: 0.7765 |
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- Accuracy: 0.9736 |
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## Model description |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Token%20Classification/Monolingual/EMBO-BLURB/NER%20Project%20Using%20EMBO-BLURB%20Dataset.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://huggingface.co/datasets/EMBO/BLURB |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Gene Precision | Gene Recall | Gene F1 | Gene Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:---------:|:---------:|:---------:|:-----------------:|:--------------:|:------:|:------:| |
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| 0.0882 | 1.0 | 786 | 0.0771 | 0.7383 | 0.7538 | 0.7460 | 6325 | 0.7383 | 0.7538 | 0.7460 | 0.9697 | |
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| 0.0547 | 2.0 | 1572 | 0.0823 | 0.7617 | 0.7758 | 0.7687 | 6325 | 0.7617 | 0.7758 | 0.7687 | 0.9732 | |
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| 0.0356 | 3.0 | 2358 | 0.0813 | 0.7521 | 0.8025 | 0.7765 | 6325 | 0.7521 | 0.8025 | 0.7765 | 0.9736 | |
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*All values in the above chart are rounded to the nearest ten-thousandth. |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
