Update README.md

README.md

@@ -1,11 +1,39 @@
 ---
 license: bigscience-openrail-m
 widget:
-- text: M[MASK]ESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGK
+- text: M[MASK]LWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN
 datasets:
 - Ensembl
 pipeline_tag: fill-mask
 tags:
 - biology
 - medical
----
+---
+
+# BERT base for proteins
+This is bidirectional transformer pretrained on amino-acid sequences of human proteins. 
+
+Example: Insulin (P01308)
+```
+MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN
+```
+
+The model was trained using the masked-language-modeling objective.
+
+## Intended uses
+This model is primarily aimed at being fine-tuned on the following tasks:
+- protein function
+- molecule-to-gene-expression mapping
+- cell targeting
+
+## How to use in your code
+```python
+from transformers import BertTokenizerFast, BertModel
+checkpoint = 'unikei/bert-base-proteins'
+tokenizer = BertTokenizerFast.from_pretrained(checkpoint)
+model = BertModel.from_pretrained(checkpoint)
+
+example = 'MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN'
+tokens = tokenizer(example, return_tensors='pt')
+predictions = model(**tokens)
+```