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gene_finetuned

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gene_finetuned
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---
license: mit
tags:
- generated_from_trainer
datasets:
- biocreative_gene_mention
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gene_finetuned
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: biocreative_gene_mention
      type: biocreative_gene_mention
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8389085168758926
    - name: Recall
      type: recall
      value: 0.8737864077669902
    - name: F1
      type: f1
      value: 0.8559923298178332
    - name: Accuracy
      type: accuracy
      value: 0.9581707699896856
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# gene_finetuned

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the biocreative_gene_mention dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1217
- Precision: 0.8389
- Recall: 0.8738
- F1: 0.8560
- Accuracy: 0.9582

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 157  | 0.1379          | 0.7838    | 0.8403 | 0.8111 | 0.9487   |
| No log        | 2.0   | 314  | 0.1188          | 0.8394    | 0.8642 | 0.8516 | 0.9570   |
| No log        | 3.0   | 471  | 0.1217          | 0.8389    | 0.8738 | 0.8560 | 0.9582   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2