---
language: en
tags:
- distilbert
- seq2seq
- text-classification
license: apache-2.0
datasets:
- Custom
metrics:
- Accuracy
- Precision
- Recall
widget:
- text: |-
    Alright, I will have to do that without fail!
model-index:
- name: Action_Items
  results:
  - task: 
      name: Action Item 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 Item

LABEL_1 - Action Item

## Usage
# Example 1
```python (Action Item)
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
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 (Not an Action Item)
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
text = '''
So that's going to come handy for their consumers to plan their migration and follow
'''
summarizer(text)
```

# Example 3
```python (Not an Action Item)
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
text = '''
Because what happens is , let's say you say 5th of January and our priority changes for whatever reason or there is a conflict or there is a bigger issue that we have to pull the engineering teams of , then that generally cannot be megabuck.
'''
summarizer(text)
```

# Example 4
```python (Action Item)
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
text = '''
But I think right now we need to get people excited about the highlights part and start meetings vision and not sober over rotated on transcription.
'''
summarizer(text)
```