Instructions to use dennlinger/roberta-cls-consec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dennlinger/roberta-cls-consec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dennlinger/roberta-cls-consec")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dennlinger/roberta-cls-consec") model = AutoModelForSequenceClassification.from_pretrained("dennlinger/roberta-cls-consec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from dennlinger/roberta-cls-consec: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/dennlinger/roberta-cls-consec/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://dennlinger/roberta-cls-consec/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/dennlinger/roberta-cls-consec/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- e5dcbe6ff61666fa57910177dc9b54437cd19b6d4edd556c693dc55bdc72fe20
- Size of remote file:
- 499 MB
- SHA256:
- b7f6d010900a09c2b8cbe8f36842a57be5ecaf0cd4b85fdfa7b5a10d6fba6ebf
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