Instructions to use squadgoals404/XLM-Roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use squadgoals404/XLM-Roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="squadgoals404/XLM-Roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("squadgoals404/XLM-Roberta") model = AutoModelForSequenceClassification.from_pretrained("squadgoals404/XLM-Roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from squadgoals404/XLM-Roberta: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/squadgoals404/XLM-Roberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://squadgoals404/XLM-Roberta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/squadgoals404/XLM-Roberta/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 3af4516a0b58418acfa38dad21676cb65501b71b0c3d9d3d6ce33064c01ee8e8
- Size of remote file:
- 17.1 MB
- SHA256:
- a514807cffabd8abaf028cfaffe7ff0c4f60b97ea2db80c41f14172ae6b018ca
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