Update README.md

README.md

@@ -1,35 +1,17 @@
 ---
-language:
-- en
 license: apache-2.0
-tags:
-- generated_from_trainer
 datasets:
-- glue
+- nyu-mll/glue
+- SetFit/mrpc
+language:
+- en
 metrics:
-- accuracy
-- f1
-model-index:
-- name: MRPC-bert
-  results:
-  - task:
-      type: text-classification
-      name: Text Classification
-    dataset:
-      name: GLUE MRPC
-      type: glue
-      args: mrpc
-    metrics:
-    - type: accuracy
-      value: 0.8823529411764706
-      name: Accuracy
-    - type: f1
-      value: 0.9178082191780821
-      name: F1
-  - task:
-      type: natural-language-inference
-      name: Natural Language Inference
-  
+- accuracy 0.8823529411764706
+- f1 0.9178082191780821
+library_name: transformers
+pipeline_tag: text-classification
+---
+---
 
 # bert-base-uncased-mrpc
 
@@ -48,3 +30,13 @@ The following hyperparameters were used during training:
 - Datasets 2.18.0
 - Tokenizers 0.15.0
 
+#Running model with Python
+```
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="brianhuster/MRPC-bert")
+classifier(
+    "Sentence 1. Sentence 2."
+)
+```
+Replace "Sentence 1" and "Sentence 2" with your actual input sentence. Each sentence should end with a fullstop, even if they are questions. The model will return LABEL_1 if they are are equivalent in meaning, LABEL_1 otherwise.