Create model card

README.md

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+---
+language: 
+  - en
+thumbnail: "url to a thumbnail used in social sharing"
+tags:
+- tag1
+- tag2
+license: cc
+datasets:
+- MIMIC-III 
+---
+
+## Model information:
+scibert-scivocab-uncased model finetuned using radiology reports in MIMIC-III. The task performed was to establised if various BERT models could classify MIMIC-III radiology report texts based upon two classes. Labels of [0,1] were assigne to radiology reports assocaited with an ICD9 Codes linked to a lung cancer  diagnosis = 1 and a random sample of reports which were not linked to any type of cancer diagnosis at all = 0. 
+
+## 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 MIMIC-III 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 - 
+- [MIMIC-III](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/pdf/nihms557856.pdf)
+- [scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_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/scibert-scivocab-uncased-ft-m3-lc")
+model = AutoModel.from_pretrained("sarahmiller137/scibert-scivocab-uncased-ft-m3-lc")
+```
+
+
+