
## Model information:
distilibert-base-uncased 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)
- [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)


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




{"language": "en", "license": "cc", "tags": ["token-classification", "named-entity-recognition", "multi_class_classification"], "datasets": ["ncbi_disease"], "metrics": ["precision", "recall", "f1", "accuracy"], "task": ["token-classification", "named-entity-recognition", "multi_class_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 distilbert token-classification named-entity-recognition multi_class_classification en dataset:ncbi_disease license:cc autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #named-entity-recognition #multi_class_classification #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us 


## Model information:
distilibert-base-uncased 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
- distilbert-base-uncased


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





## Model information:
distilibert-base-uncased 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
- distilbert-base-uncased ## How to use:
Load the model from the library using the following checkpoints:
TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #named-entity-recognition #multi_class_classification #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us 
 ## Model information:
distilibert-base-uncased 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
- distilbert-base-uncased ## How to use:
Load the model from the library using the following checkpoints:
TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #named-entity-recognition #multi_class_classification #en #dataset-ncbi_disease #license-cc #autotrain_compatible #endpoints_compatible #region-us 
## Model information:
distilibert-base-uncased 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
- distilbert-base-uncased## How to use:
Load the model from the library using the following checkpoints: