File size: 2,440 Bytes
8d0cc81 34e7930 201a06d 34e7930 8d0cc81 16349fa 8d0cc81 34e7930 709a1d1 8d0cc81 16349fa 8d0cc81 34e7930 16349fa 8d0cc81 16349fa 8d0cc81 34e7930 16349fa 8d0cc81 16349fa 8d0cc81 16349fa 8d0cc81 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
---
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)
``` |