Feature Extraction
sentence-transformers
Safetensors
Transformers
Russian
English
roberta
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use ai-forever/ru-en-RoSBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ai-forever/ru-en-RoSBERTa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ai-forever/ru-en-RoSBERTa") 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 ai-forever/ru-en-RoSBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ai-forever/ru-en-RoSBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("ai-forever/ru-en-RoSBERTa") model = AutoModelForMultimodalLM.from_pretrained("ai-forever/ru-en-RoSBERTa") - Inference
- Notebooks
- Google Colab
- Kaggle
Add ST configs
Browse files- 1_Pooling/config.json +10 -0
- modules.json +20 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false,
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"include_prompt": true
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}
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.models.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.models.Normalize"
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}
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]
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