Feature Extraction
Transformers
Safetensors
sentence-transformers
Russian
English
bert
sentence-similarity
SbertDistil
text-embeddings-inference
Instructions to use FractalGPT/SbertDistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FractalGPT/SbertDistil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FractalGPT/SbertDistil")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FractalGPT/SbertDistil") model = AutoModel.from_pretrained("FractalGPT/SbertDistil") - sentence-transformers
How to use FractalGPT/SbertDistil with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FractalGPT/SbertDistil") 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] - Notebooks
- Google Colab
- Kaggle
Upload 3 files
Browse files- 1_Pooling/config.json +7 -0
- 2_Dense/config.json +1 -0
- 2_Dense/pytorch_model.bin +3 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 312,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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2_Dense/config.json
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{"in_features": 312, "out_features": 384, "bias": true, "activation_function": "torch.nn.modules.linear.Identity"}
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2_Dense/pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2ec114f86b20c38324a2f4f443c11a3a90820abb575bd765dc2aa447eea55257
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size 482428
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