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 training_args.bin from salmaaidli/RoBerta: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/salmaaidli/RoBerta/resolve/main/training_args.bin
- Command line
-
hf download hf://salmaaidli/RoBerta/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/salmaaidli/RoBerta/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- a181796fa52942adfe15f34b87d7331286a119a556ea2170ef38f1c087812c15
- Size of remote file:
- 3.45 kB
- SHA256:
- 3c8da7c193dc72744ca2e7965886caa654af2e9609c553e4aa1ce54b83fe9d3c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.