Instructions to use subbareddyiiit/roberta_csl_gold8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subbareddyiiit/roberta_csl_gold8k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subbareddyiiit/roberta_csl_gold8k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subbareddyiiit/roberta_csl_gold8k") model = AutoModelForMaskedLM.from_pretrained("subbareddyiiit/roberta_csl_gold8k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from subbareddyiiit/roberta_csl_gold8k: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/subbareddyiiit/roberta_csl_gold8k/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://subbareddyiiit/roberta_csl_gold8k/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/subbareddyiiit/roberta_csl_gold8k/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 7a294f5ad580cb42b3d44d262abdfa94a8f9f0f9e567919a5ef16904c289cc27
- Size of remote file:
- 499 MB
- SHA256:
- befc5e4f49d4d43c4261cee5f6ac77a7f61a5e591be1e233c7d18bc994c24d6f
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