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--- |
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language: en |
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tags: |
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- distilbert |
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- seq2seq |
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- text-classification |
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license: apache-2.0 |
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datasets: |
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- Custom |
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metrics: |
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- Accuracy |
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- Precision |
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- Recall |
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widget: |
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- text: |- |
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Let's start the project as soon as possible as we are running out of deadline. |
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model-index: |
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- name: Action_Items |
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results: |
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- task: |
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name: Action Item Classification |
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type: text-classification |
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dataset: |
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name: Custom |
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type: custom |
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metrics: |
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- name: Validation Accuracy |
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type: accuracy |
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value: |
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- name: Validation Precision |
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type: precision |
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value: |
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- name: Validation Recall |
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type: recall |
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value: |
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- name: Test Accuracy |
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type: accuracy |
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value: |
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- name: Test Precision |
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type: precision |
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value: |
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- name: Test Recall |
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type: recall |
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value: |
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--- |
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Model obtained by Fine Tuning 'distilbert' using Custom Dataset! |
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LABEL_0 - Not an Action Item |
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LABEL_1 - Action Item |
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## Usage |
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# Example 1 |
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```python |
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from transformers import pipeline |
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summarizer = pipeline("text-classification", model="knkarthick/Action_Items") |
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text = ''' |
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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. |
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''' |
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summarizer(text) |
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``` |
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# Example 2 |
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```python |
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from transformers import pipeline |
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summarizer = pipeline("text-classification", model="knkarthick/Action_Items") |
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text = ''' |
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India, officially the Republic of India, is a country in South Asia. |
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''' |
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summarizer(text) |
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``` |
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# Example 3 |
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```python |
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from transformers import pipeline |
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summarizer = pipeline("text-classification", model="knkarthick/Action_Items") |
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text = ''' |
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We have been running the business successfully for over a decade now. |
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''' |
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summarizer(text) |
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``` |
