Instructions to use KennethEnevoldsen/dacy-large-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KennethEnevoldsen/dacy-large-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KennethEnevoldsen/dacy-large-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KennethEnevoldsen/dacy-large-encoder") model = AutoModel.from_pretrained("KennethEnevoldsen/dacy-large-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KennethEnevoldsen/dacy-large-encoder: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/KennethEnevoldsen/dacy-large-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KennethEnevoldsen/dacy-large-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KennethEnevoldsen/dacy-large-encoder/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 5bb786df54e4a0ebd9c6f8c997dc9ec734ec9bbfce6784050d4faa6cf4180288
- Size of remote file:
- 1.42 GB
- SHA256:
- 11df39ce92b7d8b284df4747c38fd64e056808a5c88f29d1e3136b32a12defb6
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