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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subbareddyiiit/bert_csl_gold8k") model = AutoModelForMaskedLM.from_pretrained("subbareddyiiit/bert_csl_gold8k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9dcc7bdfbcde0d8a9299034a1b93df947cfccd514c8822300faac1d638b7288d
- Size of remote file:
- 433 MB
- SHA256:
- 9d27f4ce744dd2516d70905bffe6c2d777fd98426627898f0ab535a032463c8b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.