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--- |
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license: apache-2.0 |
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language: |
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- en |
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- az |
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base_model: |
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- sentence-transformers/LaBSE |
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pipeline_tag: sentence-similarity |
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--- |
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# Small LaBSE for English-Azerbaijani |
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This is an optimized version of [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) |
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# Benchmark |
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| STSBenchmark | biosses-sts | sickr-sts | sts12-sts | sts13-sts | sts15-sts | sts16-sts | Average Pearson | Model | |
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|--------------|-------------|-----------|-----------|-----------|-----------|-----------|-----------------|--------------------------------------| |
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| 0.7363 | 0.8148 | 0.7067 | 0.7050 | 0.6535 | 0.7514 | 0.7070 | 0.7250 | sentence-transformers/LaBSE | |
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| 0.7400 | 0.8216 | 0.6946 | 0.7098 | 0.6781 | 0.7637 | 0.7222 | 0.7329 | LocalDoc/LaBSE-small-AZ | |
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| 0.5830 | 0.2486 | 0.5921 | 0.5593 | 0.5559 | 0.5404 | 0.5289 | 0.5155 | antoinelouis/colbert-xm | |
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| 0.7572 | 0.8139 | 0.7328 | 0.7646 | 0.6318 | 0.7542 | 0.7092 | 0.7377 | intfloat/multilingual-e5-large-instruct | |
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| 0.7485 | 0.7714 | 0.7271 | 0.7170 | 0.6496 | 0.7570 | 0.7255 | 0.7280 | intfloat/multilingual-e5-large | |
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| 0.6960 | 0.8185 | 0.6950 | 0.6752 | 0.5899 | 0.7186 | 0.6790 | 0.6960 | intfloat/multilingual-e5-base | |
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| 0.7376 | 0.7917 | 0.7190 | 0.7441 | 0.6286 | 0.7461 | 0.7026 | 0.7242 | intfloat/multilingual-e5-small | |
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| 0.7927 | 0.6672 | 0.7758 | 0.8122 | 0.7312 | 0.7831 | 0.7416 | 0.7577 | BAAI/bge-m3 | |
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[STS-Benchmark](https://github.com/LocalDoc-Azerbaijan/STS-Benchmark) |
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## How to Use |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("LocalDoc/LaBSE-small-AZ") |
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model = AutoModel.from_pretrained("LocalDoc/LaBSE-small-AZ") |
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# Prepare texts |
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texts = [ |
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"Hello world", |
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"Salam dünya" |
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] |
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# Tokenize and generate embeddings |
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encoded = tokenizer(texts, padding=True, truncation=True, return_tensors="pt") |
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with torch.no_grad(): |
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embeddings = model(**encoded).pooler_output |
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# Compute similarity |
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similarity = torch.nn.functional.cosine_similarity(embeddings[0], embeddings[1], dim=0) |
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``` |
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