Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/InLegalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/InLegalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/InLegalBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/InLegalBERT") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/InLegalBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45c4ac6caa54efbead4ac894bfe6e643cea2327d41c22b08def0d38ded74082e
- Size of remote file:
- 876 MB
- SHA256:
- 760504546b959f0efe346ef718356b9b26470a2dcd2310e0b8833710e202e196
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.