Instructions to use gargam/roberta-base-crest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gargam/roberta-base-crest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gargam/roberta-base-crest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gargam/roberta-base-crest") model = AutoModelForSequenceClassification.from_pretrained("gargam/roberta-base-crest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from gargam/roberta-base-crest: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/gargam/roberta-base-crest/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://gargam/roberta-base-crest/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/gargam/roberta-base-crest/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 541116bd8da4123454147351079aeb5c8e83f4a9eb48cff82d37368921b7e4be
- Size of remote file:
- 499 MB
- SHA256:
- a591f797f4202f3d5e3564d19fedc5674a002e61d32f0a38cc92e2e1a96048fe
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