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library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-sci-units-ner
results: []
datasets:
- bowenxian/BioProBench
---
<!-- 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. -->
# bert-base-cased-sci-units-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the PQA part of the
[bowenxian/BioProBench](https://huggingface.co/datasets/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
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0684 | 1.0 | 682 | 0.0268 | 0.9814 | 0.9765 | 0.9790 | 0.9937 |
| 0.0194 | 2.0 | 1364 | 0.0195 | 0.9870 | 0.9837 | 0.9853 | 0.9954 |
| 0.0067 | 3.0 | 2046 | 0.0175 | 0.9873 | 0.9867 | 0.9870 | 0.9962 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2 |