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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_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
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
from transformers import pipeline
summarizer = pipeline("text-classification", model="knkarthick/Action_Items")
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_Items")
text = '''
We have been running the business successfully for over a decade now.
'''
summarizer(text)
``` |