How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("token-classification", model="Randomui/gene_finetuned")
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Randomui/gene_finetuned")
model = AutoModelForTokenClassification.from_pretrained("Randomui/gene_finetuned", device_map="auto")
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gene_finetuned

This model is a fine-tuned version of 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
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Evaluation results