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--- |
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license: mit |
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base_model: microsoft/deberta-v3-base |
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tags: |
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- generated_from_trainer |
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- medical |
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model-index: |
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- name: deberta-med-ner-2 |
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results: [] |
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widget: |
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- text: 63 year old woman with history of CAD presented to ER |
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example_title: Example-1 |
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- text: 63 year old woman diagnosed with CAD |
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example_title: Example-2 |
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- text: >- |
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A 48 year-old female presented with vaginal bleeding and abnormal Pap |
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smears. Upon diagnosis of invasive non-keratinizing SCC of the cervix, she |
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underwent a radical hysterectomy with salpingo-oophorectomy which |
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demonstrated positive spread to the pelvic lymph nodes and the parametrium. |
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Pathological examination revealed that the tumour also extensively involved |
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the lower uterine segment. |
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example_title: example 3 |
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pipeline_tag: token-classification |
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--- |
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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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# deberta-med-ner-2 |
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This model is a fine-tuned version of [DeBERTa](https://huggingface.co/microsoft/deberta-v3-base) on the PubMED Dataset. |
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## Model description |
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Medical NER Model finetuned on BERT to recognize 41 Medical entities. |
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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: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 30 |
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- mixed_precision_training: Native AMP |
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## Usage |
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The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library. |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("token-classification", model="Clinical-AI-Apollo/Medical-NER", aggregation_strategy='simple') |
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result = pipe('45 year old woman diagnosed with CAD') |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("Clinical-AI-Apollo/Medical-NER") |
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model = AutoModelForTokenClassification.from_pretrained("Clinical-AI-Apollo/Medical-NER") |
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``` |
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### Author |
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Author: [Saketh Mattupalli](https://huggingface.co/blaze999) |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
