Update README.md

README.md

@@ -8,14 +8,22 @@ widget:
   text: "she present paper today"
 ---
 
-
-This model evaluates the wellformedness (non-fragment, grammatically correct)  score of a sentence. Model is case-sensitive and penalises for incorrect case and grammar as well. 
-
-['She is presenting a paper tomorrow','she is presenting a paper tomorrow','She present paper today']
-
-[[0.8917],[0.4270],[0.0134]]
-
-
+model_card:
+  model_name: Salesken's Query Wellformedness Score Model
+  description: Evaluate the well-formedness of sentences by checking grammatical correctness and completeness. Sensitive to case and penalizes sentences for incorrect grammar and case.
+  features:
+    - Wellformedness Score: Provides a score indicating grammatical correctness and completeness.
+    - Case Sensitivity: Recognizes and penalizes incorrect casing in sentences.
+    - Broad Applicability: Can be used on a wide range of sentences.
+  evaluation_results:
+    input:
+      - She is presenting a paper tomorrow
+      - she is presenting a paper tomorrow
+      - She present paper today
+    scores:
+      - 0.8917
+      - 0.4270
+      - 0.0134
 
 1. Dogs are mammals.
 2. she loves to read books on history.
@@ -31,24 +39,32 @@ Sentence 3 is a fragment and is not well-formed.
 Sentence 4 has a subject-verb agreement error.
 
 
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-tokenizer = AutoTokenizer.from_pretrained("salesken/query_wellformedness_score")
-model = AutoModelForSequenceClassification.from_pretrained("salesken/query_wellformedness_score")
-sentences = [' what was the reason for everyone to leave the company ', 
-               ' What was the reason behind everyone leaving the company ', 
-               ' why was everybody leaving the company ', 
-               ' what was the reason to everyone leave the company ',
-               ' what be the reason for everyone to leave the company ', 
-               ' what was the reasons for everyone to leave the company ', 
-               ' what were the reasons for everyone to leave the company ']
-
-features = tokenizer(sentences,  padding=True, truncation=True, return_tensors="pt")
-model.eval()
-with torch.no_grad():
-    scores = model(**features).logits
-print(scores)
-
-```
+  example_usage:
+    library: HuggingFace transformers
+    code: |
+      from transformers import AutoTokenizer, AutoModelForSequenceClassification
+      tokenizer = AutoTokenizer.from_pretrained("salesken/query_wellformedness_score")
+      model = AutoModelForSequenceClassification.from_pretrained("salesken/query_wellformedness_score")
+      sentences = [
+          'what was the reason for everyone to leave the company', 
+          'What was the reason behind everyone leaving the company', 
+          # ... other sentences
+      ]
+      features = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
+      model.eval()
+      with torch.no_grad():
+          scores = model(**features).logits
+      print(scores)
+  intended_use_cases:
+    - Content Creation: Validate the well-formedness of written content.
+    - Educational Platforms: Students check the grammaticality of sentences.
+    - Chatbots & Virtual Assistants: Validate user queries or generate well-formed responses.
+  limitations:
+    - Model might have occasional inaccuracies or biases.
+    - Long sentences might be truncated, affecting score.
+  contact: [email protected]
+
+
+