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 config.json from paulhindemith/fasttext-jp-embedding: direct link, hf CLI and curl.
- Browser
- Download file 375 Bytes
-
https://huggingface.co/paulhindemith/fasttext-jp-embedding/resolve/main/config.json
- Command line
-
hf download hf://paulhindemith/fasttext-jp-embedding/config.json
-
curl -L -o config.json https://huggingface.co/paulhindemith/fasttext-jp-embedding/resolve/main/config.json
375 Bytes
| { | |
| "architectures": [ | |
| "FastTextJpModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "fasttext_jp_embedding.FastTextJpConfig", | |
| "AutoModel": "fasttext_jp_embedding.FastTextJpModel" | |
| }, | |
| "hidden_size": 300, | |
| "model_type": "fasttext_jp", | |
| "tokenizer_class": "FastTextJpTokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.23.1", | |
| "vocab_size": 2000000 | |
| } | |