Instructions to use jfarray/Model_bert-base-multilingual-uncased_5_Epochs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jfarray/Model_bert-base-multilingual-uncased_5_Epochs with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jfarray/Model_bert-base-multilingual-uncased_5_Epochs") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jfarray/Model_bert-base-multilingual-uncased_5_Epochs: direct link, hf CLI and curl.
- Browser
- Download file 2.56 MB
-
https://huggingface.co/jfarray/Model_bert-base-multilingual-uncased_5_Epochs/resolve/main/tokenizer.json
- Command line
-
hf download hf://jfarray/Model_bert-base-multilingual-uncased_5_Epochs/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/jfarray/Model_bert-base-multilingual-uncased_5_Epochs/resolve/main/tokenizer.json
2.56 MB
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