Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Italian
legal ruling
Generated from Trainer
text-embeddings-inference
Instructions to use ribesstefano/RuleBert-v0.3-k4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ribesstefano/RuleBert-v0.3-k4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ribesstefano/RuleBert-v0.3-k4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ribesstefano/RuleBert-v0.3-k4") model = AutoModelForSequenceClassification.from_pretrained("ribesstefano/RuleBert-v0.3-k4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d6f4e9da87e9e1fb6c6eba62e7e2a9a6e903ac05e039858c9fb2a6b90a27c66
- Size of remote file:
- 4.79 kB
- SHA256:
- 27789edfbdd110ecf9ff246502827135d1b7713c7980c92da400f80c0d88e264
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.