Sentence Similarity
sentence-transformers
Safetensors
Transformers
Polish
roberta
feature-extraction
information-retrieval
custom_code
text-embeddings-inference
Instructions to use JakubJanusz/roberta_large_v2_ownRep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JakubJanusz/roberta_large_v2_ownRep with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("JakubJanusz/roberta_large_v2_ownRep", trust_remote_code=True) sentences = [ "[query]: Jak dożyć 100 lat?", "Trzeba zdrowo się odżywiać i uprawiać sport.", "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use JakubJanusz/roberta_large_v2_ownRep with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JakubJanusz/roberta_large_v2_ownRep", trust_remote_code=True) model = AutoModel.from_pretrained("JakubJanusz/roberta_large_v2_ownRep", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
File size: 297 Bytes
dca2e94 | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 1024,
"pooling_mode_cls_token": true,
"pooling_mode_mean_tokens": false,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |