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