Sentence Similarity
sentence-transformers
PyTorch
Transformers
Divehi
bert
feature-extraction
text-embeddings-inference
Instructions to use ashraq/tsdae-bert-base-dv-news-title with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ashraq/tsdae-bert-base-dv-news-title with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ashraq/tsdae-bert-base-dv-news-title") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use ashraq/tsdae-bert-base-dv-news-title with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ashraq/tsdae-bert-base-dv-news-title") model = AutoModel.from_pretrained("ashraq/tsdae-bert-base-dv-news-title", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ashraq/tsdae-bert-base-dv-news-title: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/ashraq/tsdae-bert-base-dv-news-title/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ashraq/tsdae-bert-base-dv-news-title/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ashraq/tsdae-bert-base-dv-news-title/resolve/main/pytorch_model.bin
266 MB
- Xet hash:
- 87ce609e3f0a85914309bb509f6f8aad40be694311b74f34d6ea38fe459d55cd
- Size of remote file:
- 266 MB
- SHA256:
- 859c0e15eaedfe27dd24e46480e763f058c127a051761d2dcb961b8b1e6d9bc5
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