Instructions to use s50227harry/TCFD-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s50227harry/TCFD-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="s50227harry/TCFD-BERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("s50227harry/TCFD-BERT") model = AutoModelForMaskedLM.from_pretrained("s50227harry/TCFD-BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from s50227harry/TCFD-BERT: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/s50227harry/TCFD-BERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://s50227harry/TCFD-BERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/s50227harry/TCFD-BERT/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- 99037d3b406f45b80b7f7c9ea259e975273ea0039fed2ec6a3b933053e64fb45
- Size of remote file:
- 329 MB
- SHA256:
- b6eb87ba3b94ea6c63d4ba3c5220283fbeb82608b05703ca1c8910a3a311e691
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