
This is a SciBERT-based model fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects. 
![model image](https://raw.githubusercontent.com/jsylee/personal-projects/master/Hugging%20Face%20ADR%20Fine-Tuning/hf_adr.png)

This model classifies input tokens into one of five classes:

- `B-DRUG`: beginning of a drug entity
- `I-DRUG`: within a drug entity
- `B-EFFECT`: beginning of an AE entity
- `I-EFFECT`: within an AE entity
- `O`: outside either of the above entities 

To get started using this model for inference, simply set up an NER `pipeline` like below: 

```python
from transformers import (AutoModelForTokenClassification, 
                          AutoTokenizer, 
                          pipeline,
                          )

model_checkpoint = "jsylee/scibert_scivocab_uncased-finetuned-ner"
model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=5,
                                                        id2label={0: 'O', 1: 'B-DRUG', 2: 'I-DRUG', 3: 'B-EFFECT', 4: 'I-EFFECT'} 
                                                        )                                                        
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

model_pipeline = pipeline(task="ner", model=model, tokenizer=tokenizer)

print( model_pipeline ("Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug."))
``` 

SciBERT: https://huggingface.co/allenai/scibert_scivocab_uncased

Dataset: https://huggingface.co/datasets/ade_corpus_v2

{"language": ["en"], "tags": ["Named Entity Recognition", "SciBERT", "Adverse Effect", "Drug", "Medical"], "datasets": ["ade_corpus_v2", "tner/bc5cdr", "commanderstrife/jnlpba", "bc2gm_corpus", "drAbreu/bc4chemd_ner", "linnaeus", "chintagunta85/ncbi_disease"], "widget": [{"text": "Abortion, miscarriage or uterine hemorrhage associated with misoprostol (Cytotec), a labor-inducing drug.", "example_title": "Abortion, miscarriage, ..."}, {"text": "Addiction to many sedatives and analgesics, such as diazepam, morphine, etc.", "example_title": "Addiction to many..."}, {"text": "Birth defects associated with thalidomide", "example_title": "Birth defects associated..."}, {"text": "Bleeding of the intestine associated with aspirin therapy", "example_title": "Bleeding of the intestine..."}, {"text": "Cardiovascular disease associated with COX-2 inhibitors (i.e. Vioxx)", "example_title": "Cardiovascular disease..."}]}
transformers pytorch bert token-classification Named Entity Recognition SciBERT Adverse Effect Drug Medical en dataset:ade_corpus_v2 dataset:tner/bc5cdr dataset:commanderstrife/jnlpba dataset:bc2gm_corpus dataset:drAbreu/bc4chemd_ner dataset:linnaeus dataset:chintagunta85/ncbi_disease autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #Named Entity Recognition #SciBERT #Adverse Effect #Drug #Medical #en #dataset-ade_corpus_v2 #dataset-tner/bc5cdr #dataset-commanderstrife/jnlpba #dataset-bc2gm_corpus #dataset-drAbreu/bc4chemd_ner #dataset-linnaeus #dataset-chintagunta85/ncbi_disease #autotrain_compatible #endpoints_compatible #region-us 


This is a SciBERT-based model fine-tuned to perform Named Entity Recognition for drug names and adverse drug effects. 
!model image

This model classifies input tokens into one of five classes:

- 'B-DRUG': beginning of a drug entity
- 'I-DRUG': within a drug entity
- 'B-EFFECT': beginning of an AE entity
- 'I-EFFECT': within an AE entity
- 'O': outside either of the above entities 

To get started using this model for inference, simply set up an NER 'pipeline' like below: 

 

SciBERT: URL

Dataset: URL

TAGS
#transformers #pytorch #bert #token-classification #Named Entity Recognition #SciBERT #Adverse Effect #Drug #Medical #en #dataset-ade_corpus_v2 #dataset-tner/bc5cdr #dataset-commanderstrife/jnlpba #dataset-bc2gm_corpus #dataset-drAbreu/bc4chemd_ner #dataset-linnaeus #dataset-chintagunta85/ncbi_disease #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #token-classification #Named Entity Recognition #SciBERT #Adverse Effect #Drug #Medical #en #dataset-ade_corpus_v2 #dataset-tner/bc5cdr #dataset-commanderstrife/jnlpba #dataset-bc2gm_corpus #dataset-drAbreu/bc4chemd_ner #dataset-linnaeus #dataset-chintagunta85/ncbi_disease #autotrain_compatible #endpoints_compatible #region-us 
