Instructions to use evm-alpha/semantic-evm-mlm-chkp1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evm-alpha/semantic-evm-mlm-chkp1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="evm-alpha/semantic-evm-mlm-chkp1000")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("evm-alpha/semantic-evm-mlm-chkp1000") model = AutoModelForMaskedLM.from_pretrained("evm-alpha/semantic-evm-mlm-chkp1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8c6e0a2a7a352816221a7bbd466285b57b00d1957ef4e3377dc0f157c88db85
- Size of remote file:
- 57.1 MB
- SHA256:
- 9fa0a0af5247d2638f59580aca10786db303024bc877ee5c4d0700a06742af46
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