Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use luluw/Roberta-devangari-script-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luluw/Roberta-devangari-script-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="luluw/Roberta-devangari-script-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("luluw/Roberta-devangari-script-classification") model = AutoModelForSequenceClassification.from_pretrained("luluw/Roberta-devangari-script-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad52728b3365f92d134e85ae73add09cf7d89ed387f8e095087b04dcb25ef0ed
- Size of remote file:
- 5.24 kB
- SHA256:
- 40b185b8e63f7cdd2c9e6363907378d4111532cf1f65dfcfbb58b5a0e5515cb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.