Instructions to use philschmid/gpu-xlm-roberta-large-amazon-massive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/gpu-xlm-roberta-large-amazon-massive with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/gpu-xlm-roberta-large-amazon-massive")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/gpu-xlm-roberta-large-amazon-massive") model = AutoModelForSequenceClassification.from_pretrained("philschmid/gpu-xlm-roberta-large-amazon-massive", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from philschmid/gpu-xlm-roberta-large-amazon-massive: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/philschmid/gpu-xlm-roberta-large-amazon-massive/resolve/main/tokenizer.json
- Command line
-
hf download hf://philschmid/gpu-xlm-roberta-large-amazon-massive/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/philschmid/gpu-xlm-roberta-large-amazon-massive/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 919657d3b0844715e814cbb3de4e727ef558a55c7ef80475f3089746aa2cb53d
- Size of remote file:
- 17.1 MB
- SHA256:
- affcfb1f45c4b14a70a6589c3d153b430ed4309e5a6613a88dab64d5a923a5d6
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