README.md

metadata

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: null
          - name: Validation Precision
            type: precision
            value: null
          - name: Validation Recall
            type: recall
            value: null
          - name: Test Accuracy
            type: accuracy
            value: null
          - name: Test Precision
            type: precision
            value: null
          - name: Test Recall
            type: recall
            value: null

Model obtained by Fine Tuning 'distilbert' using Custom Dataset!

LABEL_0 - Not an Action Item

LABEL_1 - Action Item

Usage

Example 1

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

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

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

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)