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
File size: 375 Bytes
76b4794 3ba50ba 76b4794 912b503 76b4794 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"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
}
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