Instructions to use msharma95/hml-es-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-es-1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-es-1000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-es-1000") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-es-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8d3251d1c2763f14494f203c11700be1cc13e7b8804f0f4ba5bca2ee8297a30
- Size of remote file:
- 68.3 MB
- SHA256:
- a1461c21d137bb90421faedfa00161b877138ea1f5b4682be9d1009c8e0edbb4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.