Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use xshubhamx/roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xshubhamx/roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xshubhamx/roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xshubhamx/roberta-base") model = AutoModelForSequenceClassification.from_pretrained("xshubhamx/roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29dbad2ecd7a7975fe10811d137e6367caea570432d3c02a3a67070923e0d045
- Size of remote file:
- 997 MB
- SHA256:
- eb0531ba0801cbddadf33dc28120bfc0e875f336bd3a329e0074359b32fe1b2d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.