Instructions to use d4data/EnviroDueDiligence_LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d4data/EnviroDueDiligence_LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="d4data/EnviroDueDiligence_LM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("d4data/EnviroDueDiligence_LM") model = AutoModelForMaskedLM.from_pretrained("d4data/EnviroDueDiligence_LM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb443db28082e2abdb6e2a56b96266da2f6d692ab8753c4628c45e7d421e7d2f
- Size of remote file:
- 3.77 kB
- SHA256:
- 8b59099cda019e5c0660b87df9450ef60c4252871816b4bc4ed74ce07c6e1d00
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