bert-base-cased-sci-units-ner

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bert-base-cased-sci-units-ner

This model is a fine-tuned version of bert-base-cased on the PQA part of the bowenxian/BioProBench dataset It achieves the following results on the evaluation set:

• Loss: 0.0175
• Precision: 0.9873
• Recall: 0.9867
• F1: 0.9870
• Accuracy: 0.9962

Model description

The model has been trained to perform token classification task by training the bert-base-cased model. The tokens to be classified correspond to the values and units of scientific measurements.

For example in the sentence:

"Place the seeds in a refrigerator at 4°C along with a small amount of water for 2-3 days."

The model will select "4°C" and identify the value as 4 and the unit as °C

"Centrifuge at 863g for 5 min at room temperature (18–28°C), decant supernatant and resuspend cells in culture medium."

The model will identify to value-unit combinations:

• VALUE : 863, UNIT: g
• VALUE : 18 - 28, UNIT: '°C'

Intended uses & limitations

Identify VALUES and scientific UNITS from a sentence.

This is a work in progress and currently only identifies the units:

• Temperature: '°C'
• Mass (grams): 'g, ug, mg'
• Volume (L): 'L, uL, mL'

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2