Sentence Similarity
sentence-transformers
Safetensors
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use danfeg/CAMeL_Finetuned-EN-2481 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/CAMeL_Finetuned-EN-2481 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/CAMeL_Finetuned-EN-2481") 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] - Transformers
How to use danfeg/CAMeL_Finetuned-EN-2481 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danfeg/CAMeL_Finetuned-EN-2481") model = AutoModel.from_pretrained("danfeg/CAMeL_Finetuned-EN-2481", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from danfeg/CAMeL_Finetuned-EN-2481: direct link, hf CLI and curl.
- Browser
- Download file 776 kB
-
https://huggingface.co/danfeg/CAMeL_Finetuned-EN-2481/resolve/main/tokenizer.json
- Command line
-
hf download hf://danfeg/CAMeL_Finetuned-EN-2481/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/danfeg/CAMeL_Finetuned-EN-2481/resolve/main/tokenizer.json
776 kB
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