Instructions to use msharma95/hml-es-7000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msharma95/hml-es-7000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="msharma95/hml-es-7000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("msharma95/hml-es-7000") model = AutoModelForMaskedLM.from_pretrained("msharma95/hml-es-7000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5082f5ffe18297f644e74ecbb90fea0120502fd389eeede9c0766528615b5f15
- Size of remote file:
- 68.3 MB
- SHA256:
- ccc3e025fbad9847d303c88c0e773ab2a5756519474e7d620e131f987b9b567b
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