Instructions to use subbareddyiiit/bert_csl_gold8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subbareddyiiit/bert_csl_gold8k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subbareddyiiit/bert_csl_gold8k")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subbareddyiiit/bert_csl_gold8k") model = AutoModelForMaskedLM.from_pretrained("subbareddyiiit/bert_csl_gold8k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from subbareddyiiit/bert_csl_gold8k: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/subbareddyiiit/bert_csl_gold8k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://subbareddyiiit/bert_csl_gold8k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/subbareddyiiit/bert_csl_gold8k/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 9c3aefb7463bc73c65a60ecea2aad555b6529796dd2ad165ac0784bcb7421aa4
- Size of remote file:
- 433 MB
- SHA256:
- 8fabd06bd329d8b24d2f1563c86411c93afe18d408726484a17ec9e3d8783429
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