Instructions to use paulhindemith/fasttext-jp-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paulhindemith/fasttext-jp-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="paulhindemith/fasttext-jp-embedding", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("paulhindemith/fasttext-jp-embedding", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from paulhindemith/fasttext-jp-embedding: direct link, hf CLI and curl.
- Browser
- Download file 2.4 GB
-
https://huggingface.co/paulhindemith/fasttext-jp-embedding/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://paulhindemith/fasttext-jp-embedding/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/paulhindemith/fasttext-jp-embedding/resolve/main/pytorch_model.bin
2.4 GB
- Xet hash:
- 99a79cbf133c69790c2249f809f06ca223b60acca79927b3f12e65b1a268494d
- Size of remote file:
- 2.4 GB
- SHA256:
- ba58a6e9bba7142a3d3507fc094345ae2e5ebb222fe98cdf5b2146487895314e
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