Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use marcolatella/prova_Classi2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcolatella/prova_Classi2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcolatella/prova_Classi2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcolatella/prova_Classi2") model = AutoModelForSequenceClassification.from_pretrained("marcolatella/prova_Classi2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from marcolatella/prova_Classi2: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/marcolatella/prova_Classi2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://marcolatella/prova_Classi2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/marcolatella/prova_Classi2/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 858d567431df8714553593eb6bf702a25db8ccd7c13e18089ecbb693eecd494e
- Size of remote file:
- 268 MB
- SHA256:
- a38645eff5ce68b0ef72e79be0b6c8d677facefdea485e564d780304f9ebcd2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.