Sentence Similarity
sentence-transformers
Safetensors
Transformers
Italian
English
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use nickprock/xlmr-ted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nickprock/xlmr-ted with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nickprock/xlmr-ted") sentences = [ "Questa è una persona felice", "Questo è un cane felice", "Questa è una persona molto felice", "Oggi è una giornata di sole" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use nickprock/xlmr-ted with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nickprock/xlmr-ted") model = AutoModel.from_pretrained("nickprock/xlmr-ted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nickprock/xlmr-ted: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/nickprock/xlmr-ted/resolve/main/tokenizer.json
- Command line
-
hf download hf://nickprock/xlmr-ted/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nickprock/xlmr-ted/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- be42ccf98ca5396fe1d8f9d3ba7ef5b34f131800e4ed70e3aa7d27048fc47c01
- Size of remote file:
- 17.1 MB
- SHA256:
- f7bc482bbe4d17395039e8c52dfd60092475c46bdcd8cd16c7238b2c522cd2c8
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