Instructions to use FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL") model = AutoModelForSequenceClassification.from_pretrained("FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL/resolve/main/tokenizer.json
- Command line
-
hf download hf://FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/FINDA-FIT/XLM-ROBERTA_LARGE_FALSE_FALSE_FALSE_FULL/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- fb783db9d1b9e2663d8320803a80ecc3a9347e3123793f985f33aaf76f4e8b07
- Size of remote file:
- 17.1 MB
- SHA256:
- b83050ca499108b9cd6b0fca42f13c95713dd3b5db7238c8703136a5ed0a2b3e
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