Text Classification
Transformers
Safetensors
distilbert
prompt-routing
llm-router
nadirclaw
text-embeddings-inference
Instructions to use nadirclaw/nadirclaw-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nadirclaw/nadirclaw-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nadirclaw/nadirclaw-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nadirclaw/nadirclaw-distilbert") model = AutoModelForSequenceClassification.from_pretrained("nadirclaw/nadirclaw-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31aafb4fbb4d718969af35f50f125d026ade1f74f8cd1bd0e3024b1c8ca0a247
- Size of remote file:
- 268 MB
- SHA256:
- 3a6f8ec0df75f737b4bb9d2ac41ac251dabc6cff2d145bf5815fffc96bda78a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.