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update model card README.md
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README.md
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---
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tags:
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- generated_from_trainer
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datasets:
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- jnlpba
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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: biobert-finetuned-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: jnlpba
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type: jnlpba
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config: jnlpba
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split: train
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args: jnlpba
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metrics:
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- name: Precision
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type: precision
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value: 0.6550939663699308
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- name: Recall
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type: recall
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value: 0.7646040175479104
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- name: F1
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type: f1
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value: 0.7056253995312167
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- name: Accuracy
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type: accuracy
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value: 0.9107839603371846
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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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# biobert-finetuned-ner
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This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the jnlpba dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5113
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- Precision: 0.6551
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- Recall: 0.7646
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- F1: 0.7056
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- Accuracy: 0.9108
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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: 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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| 0.1815 | 1.0 | 2319 | 0.2706 | 0.6538 | 0.7704 | 0.7073 | 0.9160 |
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| 0.1226 | 2.0 | 4638 | 0.3230 | 0.6524 | 0.7675 | 0.7053 | 0.9118 |
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| 0.0813 | 3.0 | 6957 | 0.3974 | 0.6483 | 0.7611 | 0.7002 | 0.9101 |
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| 0.0521 | 4.0 | 9276 | 0.4529 | 0.6575 | 0.7652 | 0.7073 | 0.9121 |
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| 0.0356 | 5.0 | 11595 | 0.5113 | 0.6551 | 0.7646 | 0.7056 | 0.9108 |
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### Framework versions
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- Transformers 4.21.1
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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