Text Classification
Transformers
PyTorch
English
distilbert
seq2seq
Eval Results (legacy)
text-embeddings-inference
Instructions to use knkarthick/Action_Items with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knkarthick/Action_Items with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="knkarthick/Action_Items")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("knkarthick/Action_Items") model = AutoModelForSequenceClassification.from_pretrained("knkarthick/Action_Items") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0e3880bf42d0fd3d79466ee67bbba03b45af20c6f5c9e5f86b780c76833c662
- Size of remote file:
- 268 MB
- SHA256:
- a57e495823e680539ba23142dbbbc23600a73a0059718a91b900f781b3f4f3b5
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