| --- |
| 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 |
