README.md

metadata

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

pombe_curation_fold_0

This model is a fine-tuned version of 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