bert-linnaeus-ner

This model is a fine-tuned version of bert-base-cased on the linnaeus dataset. It achieves the following results on the evaluation set:

• Loss: 0.0073
• Precision: 0.9223
• Recall: 0.9522
• F1: 0.9370
• Accuracy: 0.9985

Model description

This model can be used to find organisms and species in text data.

NB. THIS MODEL IS WIP AND IS SUBJECT TO CHANGE!

Intended uses & limitations

This model's intended use is in my Master's thesis to mask names of bacteria (and phages) for further analysis.

Training and evaluation data

Linnaeus dataset was used to train and validate the performance.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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
0.0076	1.0	1492	0.0128	0.8566	0.9578	0.9044	0.9967
0.0024	2.0	2984	0.0082	0.9092	0.9578	0.9329	0.9980
0.0007	3.0	4476	0.0073	0.9223	0.9522	0.9370	0.9985

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.14.5
• Tokenizers 0.14.0