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:
- 97425f8b862aca56d3f06ed01e22888e050fc6f5612e00198a3229c98f7b1977
- Size of remote file:
- 334 MB
- SHA256:
- ce2f0a16f1db5bcf32eeeebaaec00dcc1d572a40001ee0fec21f17adc169a859
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.