Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use danfeg/ST-PARA-XLM-R-M_Finetuned-COMB-4500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danfeg/ST-PARA-XLM-R-M_Finetuned-COMB-4500 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danfeg/ST-PARA-XLM-R-M_Finetuned-COMB-4500") 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/ST-PARA-XLM-R-M_Finetuned-COMB-4500 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("danfeg/ST-PARA-XLM-R-M_Finetuned-COMB-4500") model = AutoModel.from_pretrained("danfeg/ST-PARA-XLM-R-M_Finetuned-COMB-4500") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e87a9fcfcf10edaa13f7a11f1b76482d7c0b5f626134908b41f42d361f7a6e83
- Size of remote file:
- 17.1 MB
- SHA256:
- d2174406fd56fd8d5b49b5a7bc51c2a8a7986ceacaed6ad9f3ee57fbc799b84a
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