Instructions to use sentence-transformers/paraphrase-xlm-r-multilingual-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/paraphrase-xlm-r-multilingual-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/paraphrase-xlm-r-multilingual-v1") 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 sentence-transformers/paraphrase-xlm-r-multilingual-v1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/paraphrase-xlm-r-multilingual-v1") model = AutoModel.from_pretrained("sentence-transformers/paraphrase-xlm-r-multilingual-v1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
List of languages?
Is there a list of languages for which this model was trained?
Also, the link at https://seb.sbert.net/?model_name=sentence-transformers does not load.
Any updtaes here ?
Hello!
I can't be 100% sure, but I think Nils trained this following this work: https://arxiv.org/abs/2004.09813
There's more details here: https://www.sbert.net/docs/pretrained_models.html#multi-lingual-models
Which says: We used the following 50+ languages: ar, bg, ca, cs, da, de, el, en, es, et, fa, fi, fr, fr-ca, gl, gu, he, hi, hr, hu, hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, pt-br, ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh-cn, zh-tw.
Hope that helps.
- Tom Aarsen