Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
text-embeddings-inference
Instructions to use ICFNext/EYY-Categorisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ICFNext/EYY-Categorisation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ICFNext/EYY-Categorisation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ICFNext/EYY-Categorisation") model = AutoModelForSequenceClassification.from_pretrained("ICFNext/EYY-Categorisation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2000/optimizer.pt from ICFNext/EYY-Categorisation: direct link, hf CLI and curl.
- Browser
- Download file 536 MB
-
https://huggingface.co/ICFNext/EYY-Categorisation/resolve/refs%2Fpr%2F1/checkpoint-2000/optimizer.pt
- Command line
-
hf download hf://ICFNext/EYY-Categorisation@refs/pr/1/checkpoint-2000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ICFNext/EYY-Categorisation/resolve/refs%2Fpr%2F1/checkpoint-2000/optimizer.pt
536 MB
- Xet hash:
- 29fa9ed7494106093c4e35231f609113daa9167266ff590c5f40d3355edce6ca
- Size of remote file:
- 536 MB
- SHA256:
- 60a410a19fed6a5e2ed8988db4c779365319f66117c96a9fdfaf8d3a370bdaa9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.