Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use salmaaidli/RoBerta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salmaaidli/RoBerta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="salmaaidli/RoBerta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("salmaaidli/RoBerta") model = AutoModelForSequenceClassification.from_pretrained("salmaaidli/RoBerta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from salmaaidli/RoBerta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/salmaaidli/RoBerta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://salmaaidli/RoBerta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/salmaaidli/RoBerta/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 9da318e34dbf56c426d78a0fb64777ef30fb9f89be19d6ce71199c2fc5868bde
- Size of remote file:
- 499 MB
- SHA256:
- 46f8726c64de4a9acdd2b32d184b075c78e551087084140acfb10ad27c53b6f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.