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--- |
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license: apache-2.0 |
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base_model: bert-base-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- linnaeus |
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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-linnaeus-ner |
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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: linnaeus |
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type: linnaeus |
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config: linnaeus |
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split: validation |
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args: linnaeus |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9223433242506812 |
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- name: Recall |
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type: recall |
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value: 0.9521800281293952 |
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- name: F1 |
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type: f1 |
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value: 0.9370242214532872 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9985110458648063 |
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widget: |
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- text: "Streptococcus suis (S. suis) is an important zoonosis and pathogen that can carry prophages." |
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- text: "Lactobacillus plantarum is an important probiotic and is mostly isolated from fermented foods." |
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inference: |
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parameters: |
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aggregation_strategy: "first" |
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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-linnaeus-ner |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the linnaeus dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0073 |
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- Precision: 0.9223 |
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- Recall: 0.9522 |
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- F1: 0.9370 |
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- Accuracy: 0.9985 |
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## Model description |
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This model can be used to find organisms and species in text data. |
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NB. THIS MODEL IS WIP AND IS SUBJECT TO CHANGE! |
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## Intended uses & limitations |
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This model's intended use is in my Master's thesis to mask names of bacteria (and phages) for further analysis. |
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## Training and evaluation data |
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Linnaeus dataset was used to train and validate the performance. |
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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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| 0.0076 | 1.0 | 1492 | 0.0128 | 0.8566 | 0.9578 | 0.9044 | 0.9967 | |
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| 0.0024 | 2.0 | 2984 | 0.0082 | 0.9092 | 0.9578 | 0.9329 | 0.9980 | |
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| 0.0007 | 3.0 | 4476 | 0.0073 | 0.9223 | 0.9522 | 0.9370 | 0.9985 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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