Update README.md

README.md

@@ -1,3 +1,66 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- az
+base_model:
+- sentence-transformers/LaBSE
+pipeline_tag: sentence-similarity
+---
+
+---
+language:
+- en
+- az
+tags:
+- LaBSE
+- sentence-transformers
+- sentence-similarity
+- dimensionality-reduction
+- bert
+license: apache-2.0
+---
+
+# Small LaBSE for English-Azerbaijani
+
+This is an optimized version of [LaBSE (Language-agnostic BERT Sentence Embeddings)](https://huggingface.co/sentence-transformers/LaBSE) specifically for English and Azerbaijani language.
+
+
+# Benchmark
+
+| STSBenchmark | biosses-sts | sickr-sts | sts12-sts | sts13-sts | sts15-sts | sts16-sts | Average Pearson | Model                                |
+|--------------|-------------|-----------|-----------|-----------|-----------|-----------|-----------------|--------------------------------------|
+| 0.7363       | 0.8148      | 0.7067    | 0.7050    | 0.6535    | 0.7514    | 0.7070    | 0.7250          | sentence-transformers/LaBSE           |
+| 0.7400       | 0.8216      | 0.6946    | 0.7098    | 0.6781    | 0.7637    | 0.7222    | 0.7329          | LocalDoc/LaBSE-small-AZ           |
+| 0.5830       | 0.2486      | 0.5921    | 0.5593    | 0.5559    | 0.5404    | 0.5289    | 0.5155          | antoinelouis/colbert-xm               |
+| 0.7572       | 0.8139      | 0.7328    | 0.7646    | 0.6318    | 0.7542    | 0.7092    | 0.7377          | intfloat/multilingual-e5-large-instruct |
+| 0.7485       | 0.7714      | 0.7271    | 0.7170    | 0.6496    | 0.7570    | 0.7255    | 0.7280          | intfloat/multilingual-e5-large        |
+| 0.6960       | 0.8185      | 0.6950    | 0.6752    | 0.5899    | 0.7186    | 0.6790    | 0.6960          | intfloat/multilingual-e5-base         |
+| 0.7376       | 0.7917      | 0.7190    | 0.7441    | 0.6286    | 0.7461    | 0.7026    | 0.7242          | intfloat/multilingual-e5-small        |
+| 0.7927       | 0.6672      | 0.7758    | 0.8122    | 0.7312    | 0.7831    | 0.7416    | 0.7577          | BAAI/bge-m3                           |
+
+
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("LocalDoc/LaBSE-small-AZ")
+model = AutoModel.from_pretrained("LocalDoc/LaBSE-small-AZ")
+
+# Prepare texts
+texts = [
+    "Hello world",  # English
+    "Salam dünya"   # Azerbaijani
+]
+
+# Tokenize and generate embeddings
+encoded = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+with torch.no_grad():
+    embeddings = model(**encoded).pooler_output
+
+# Compute similarity
+similarity = torch.nn.functional.cosine_similarity(embeddings[0], embeddings[1], dim=0)