readme: ---
  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 Decision
  
  ## 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)
  ```