Text Classification
Transformers
PyTorch
English
distilbert
seq2seq
Eval Results (legacy)
text-embeddings-inference
Instructions to use knkarthick/Action_Decisions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use knkarthick/Action_Decisions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="knkarthick/Action_Decisions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("knkarthick/Action_Decisions") model = AutoModelForSequenceClassification.from_pretrained("knkarthick/Action_Decisions") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88d5e8d5d926c271cb7c3ddcd555beaac39536901e5a7d4d7d0899fd3f4c2fd5
- Size of remote file:
- 268 MB
- SHA256:
- e1b82449a1e5873b698ff76657b0540f4749356cca29d2263ea254d105a3e953
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.