Instructions to use msharma95/hml-es-1200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-es-1200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-es-1200")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-es-1200") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-es-1200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 871b98b5f8849563c546bf57c344cf01e68547041d669db6e12f1742ab9a0c26
- Size of remote file:
- 68.3 MB
- SHA256:
- e461a2ff3c82502faa149efc0907d5e7c091b6b430a78d2040a3db6c9f39aca0
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