Text Classification
Transformers
PyTorch
English
distilbert
seq2seq
Eval Results (legacy)
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("knkarthick/Action_Items")
model = AutoModelForSequenceClassification.from_pretrained("knkarthick/Action_Items")Quick Links
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 = '''
India, officially the Republic of India, is a country in South Asia.
'''
summarizer(text)
Example 3
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)
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Evaluation results
- Validation Accuracy on Customself-reported
- Validation Precision on Customself-reported
- Validation Recall on Customself-reported
- Test Accuracy on Customself-reported
- Test Precision on Customself-reported
- Test Recall on Customself-reported
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="knkarthick/Action_Items")