Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use sachinn1/xl-durel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sachinn1/xl-durel2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sachinn1/xl-durel2") 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 sachinn1/xl-durel2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sachinn1/xl-durel2") model = AutoModel.from_pretrained("sachinn1/xl-durel2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sachinn1/xl-durel2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/sachinn1/xl-durel2/resolve/main/tokenizer.json
- Command line
-
hf download hf://sachinn1/xl-durel2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sachinn1/xl-durel2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 0b92b757e2e19b784d7b8454f89c80234b257ac7415b0c8881e845334b5af7e5
- Size of remote file:
- 17.1 MB
- SHA256:
- ad08640384683dbef7cb485529b96e6c61fdadffc95b3e27e7aab62b3cdd0643
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