Create README.md

README.md

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+---
+language: en
+tags:
+- distilbert
+- seq2seq
+- text-classification
+license: apache-2.0
+datasets:
+- Custom
+metrics:
+- Accuracy
+- Precision
+- Recall
+widget:
+- text: |-
+    Let's start the project as soon as possible as we are running out of deadline.
+model-index:
+- name: Action_Decisions
+  results:
+  - task: 
+      name: Action Decisions Classification
+      type: text-classification
+    dataset:
+      name: Custom
+      type: custom
+      
+    metrics:
+    - name: Validation Accuracy
+      type: accuracy
+      value: 
+    - name: Validation Precision
+      type: precision
+      value: 
+    - name: Validation Recall
+      type: recall
+      value: 
+    - name: Test Accuracy
+      type: accuracy
+      value: 
+    - name: Test Precision
+      type: precision
+      value: 
+    - name: Test Recall
+      type: recall
+      value:
+---
+Model obtained by Fine Tuning 'distilbert' using Custom Dataset!
+
+LABEL_0 - Not an Action Decision
+
+LABEL_1 - Action Item
+
+## Usage
+# Example 1
+```python
+from transformers import pipeline
+summarizer = pipeline("text-classification", model="knkarthick/Action_Decisions")
+text = '''
+Customer portion will have the dependency of , you know , fifty five probably has to be on XGEVA before we can start that track , but we can at least start the enablement track for sales and CSM who are as important as customers because they're the top of our funnel , especially sales.
+'''
+summarizer(text)
+```
+# Example 2
+```python
+from transformers import pipeline
+summarizer = pipeline("text-classification", model="knkarthick/Action_Decisions")
+text = '''
+India, officially the Republic of India, is a country in South Asia.
+'''
+summarizer(text)
+```
+
+# Example 3
+```python
+from transformers import pipeline
+summarizer = pipeline("text-classification", model="knkarthick/Action_Decisions")
+text = '''
+We have been running the business successfully for over a decade now.
+'''
+summarizer(text)
+```