Update README.md

README.md

@@ -5,26 +5,17 @@ license: apache-2.0
 inference: true
 datasets: google_wellformed_query
 widget:
-  text: "she present paper today"
+  text: "She present paper today"
 ---
 
-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
+**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.
 
+**Example**:
 1. Dogs are mammals.
 2. she loves to read books on history.
 3. When the rain in Spain.
@@ -39,8 +30,8 @@ Sentence 3 is a fragment and is not well-formed.
 Sentence 4 has a subject-verb agreement error.
 
 
-  example_usage:
-    library: HuggingFace transformers
+**example_usage:**
+*library: HuggingFace transformers*
 ```python
       from transformers import AutoTokenizer, AutoModelForSequenceClassification
       tokenizer = AutoTokenizer.from_pretrained("salesken/query_wellformedness_score")
@@ -57,14 +48,15 @@ Sentence 4 has a subject-verb agreement error.
       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: contact@salesken.ai
+**Intended Use Cases**
+
+*Content Creation*: Validate the well-formedness of written content.
+
+*Educational Platforms*: Helps students check the grammaticality of their sentences.
+
+*Chatbots & Virtual Assistants*: To validate user queries or generate well-formed responses.
+
+  contact: ashish@salesken.ai