model update

README.md

@@ -1,6 +1,6 @@
 ---
 datasets:
-- btc
+- tner/btc
 metrics:
 - f1
 - precision
@@ -12,9 +12,9 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: btc
-      type: btc
-      args: btc
+      name: tner/btc
+      type: tner/btc
+      args: tner/btc
     metrics:
     - name: F1
       type: f1
@@ -78,6 +78,18 @@ For F1 scores, the confidence interval is obtained by bootstrap as below:
 Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-btc/raw/main/eval/metric.json) 
 and [metric file of entity span](https://huggingface.co/tner/roberta-large-btc/raw/main/eval/metric_span.json).
 
+### Usage
+This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip   
+```shell
+pip install tner
+```
+and activate model as below.
+```python
+from tner import TransformersNER
+model = TransformersNER("tner/roberta-large-btc")
+model.predict(["Jacob Collier is a Grammy awarded English artist from London"])
+```
+It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.
 
 ### Training hyperparameters