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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("knkarthick/Action_Items") model = AutoModelForSequenceClassification.from_pretrained("knkarthick/Action_Items", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from knkarthick/Action_Items: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/knkarthick/Action_Items/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://knkarthick/Action_Items@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/knkarthick/Action_Items/resolve/refs%2Fpr%2F2/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.