---
base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext
library_name: transformers
license: mit
pipeline_tag: text-classification
tags:
- generated_from_trainer
model-index:
- name: pombe_curation_fold_0
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: afg1/pombe-canto-data
      type: text-classification
      split: test
    metrics:
    - type: accuracy
      value: 0.9254826254826255
      name: Accuracy
    - type: recall
      value: 0.9372056514913658
      name: Recall
    - type: precision
      value: 0.9135424636572304
      name: Precision
    - type: f1
      value: 0.9252227818674932
      name: F1
    - type: total_time_in_seconds
      value: 118.32597812499444
      name: Total_Time_In_Seconds
    - type: samples_per_second
      value: 21.88868447184131
      name: Samples_Per_Second
    - type: latency_in_seconds
      value: 0.04568570583976619
      name: Latency_In_Seconds
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/afg1/pombe_curation_model/runs/richbds0)
# pombe_curation_fold_0

This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on an unknown dataset.

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.42.3
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1