Instructions to use ainewtrend07/google-rembert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ainewtrend07/google-rembert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ainewtrend07/google-rembert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ainewtrend07/google-rembert") model = AutoModelForSequenceClassification.from_pretrained("ainewtrend07/google-rembert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ainewtrend07/google-rembert: direct link, hf CLI and curl.
- Browser
- Download file 16.7 MB
-
https://huggingface.co/ainewtrend07/google-rembert/resolve/main/tokenizer.json
- Command line
-
hf download hf://ainewtrend07/google-rembert/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ainewtrend07/google-rembert/resolve/main/tokenizer.json
16.7 MB
- Xet hash:
- 07f8b113e6621001585bb90eb10c57268790ebe63ea0b8f0134532ec8db3461c
- Size of remote file:
- 16.7 MB
- SHA256:
- 77932468faa643195fd87062d3f03cba416187da354486c0b896dc6d3afb716d
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