| --- |
| license: apache-2.0 |
| language: |
| - en |
| metrics: |
| - seqeval |
| base_model: |
| - google-bert/bert-base-uncased |
| pipeline_tag: token-classification |
| library_name: transformers |
| tags: |
| - medical |
| - healthcare |
| --- |
| # Model Name: DeepNeural_NER-I |
| |
| <!-- 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-uncased |
| |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the medical-ner-bleurt-separated dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0 |
| - F1: 1.0 |
| |
| ## Model description |
| |
| The DeepNeural NER-I model is exclusively designed to identify body parts in textual documents. |
| This clinical support model is one of many to be released, and is a crucial aspect of clinical support systems. |
| |
| ## Intended uses & limitations |
| |
| The model is meant to be used for research and development purposes by Data Scientists, ML & Software Engineers for the development of NER applications |
| capable of identifying body parts in medical EHR systems to augment patient health processing. |
| |
| ## Training and evaluation data |
| |
| Training |
| |
| ## Training procedure |
| The DeepNeural_NER-I model was trained with precision and accuracy in mind, and therefore |
| the model was trained for 3 epochs and 13500 global steps per epoch. The training scores utilized |
| are highlighted in the table below. |
|
|
| | Training Method | # Score | |
| |:-------------:|:-----:| |
| | Precision | 1.0 | |
| | Recall | 1.0 | |
| | F1-Score | 1.0 | |
| | Accuracy | 1.0 | |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 24 |
| - eval_batch_size: 24 |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
| - weight_decay: 0.01 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Validation Loss | F1 | |
| |:-------------:|:-----:|:---------------:|:------:| |
| | 2.61 | 1.0 | 0.0 | 1.0 | |
| | 2.61 | 2.0 | 0.0 | 1.0 | |
| | 2.61 | 3.0 | 0.0 | 1.0 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.56.1 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.0 |
| |
