
## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library. 

## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text.  The model will predict lables based upon the NCBI-disease dataset, please see the dataset information for details.

## Limitations:
Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model - 
- [NCBI Disease](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/pdf/nihms557856.pdf)
- [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext)

## Widget text:
The text displayed in the example widget was taken from one of the ncbi datasets abstracts. 

## How to use:
Load the model from the library using the following checkpoints:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ft-ncbi-disease")
model = AutoModel.from_pretrained("sarahmiller137/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ft-ncbi-disease")
```

{"language": "en", "license": "cc", "tags": ["named-entity-recognition", "token-classification"], "datasets": "ncbi_disease", "metrics": ["precision", "recall", "f1", "accuracy"], "task": ["named-entity-recognition", "token-classification"], "widget": [{"text": " The risk of cancer, especially lymphoid neoplasias, is substantially elevated in A-T patients and has long been associated with chromosomal instability."}]}
transformers pytorch safetensors bert token-classification named-entity-recognition en dataset:ncbi_disease license:cc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #named-entity-recognition #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us 


## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library. 

## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text.  The model will predict lables based upon the NCBI-disease dataset, please see the dataset information for details.

## Limitations:
Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model - 
- NCBI Disease
- microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext

## Widget text:
The text displayed in the example widget was taken from one of the ncbi datasets abstracts. 

## How to use:
Load the model from the library using the following checkpoints:


## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library. ## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text.  The model will predict lables based upon the NCBI-disease dataset, please see the dataset information for details. ## Limitations:
Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model - 
- NCBI Disease
- microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext ## Widget text:
The text displayed in the example widget was taken from one of the ncbi datasets abstracts. ## How to use:
Load the model from the library using the following checkpoints:
TAGS
#transformers #pytorch #safetensors #bert #token-classification #named-entity-recognition #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us 
 ## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library. ## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text.  The model will predict lables based upon the NCBI-disease dataset, please see the dataset information for details. ## Limitations:
Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model - 
- NCBI Disease
- microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext ## Widget text:
The text displayed in the example widget was taken from one of the ncbi datasets abstracts. ## How to use:
Load the model from the library using the following checkpoints:
passage: TAGS
#transformers #pytorch #safetensors #bert #token-classification #named-entity-recognition #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us 
## Model information:
microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model finetuned using the ncbi_disease dataset from the datasets library.## Intended uses:
This model is intended to be used for named entity recoginition tasks. The model will identify disease entities in text.  The model will predict lables based upon the NCBI-disease dataset, please see the dataset information for details.## Limitations:
Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model - 
- NCBI Disease
- microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext## Widget text:
The text displayed in the example widget was taken from one of the ncbi datasets abstracts.## How to use:
Load the model from the library using the following checkpoints: