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LocalDoc
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TEmA-small

Sentence Similarity
PyTorch
Azerbaijani
bert
labse
Model card Files Community
2
TEmA-small
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---
license: cc-by-4.0
language:
- az
metrics:
- pearsonr
base_model:
- sentence-transformers/LaBSE
pipeline_tag: sentence-similarity
widget:
- source_sentence: Bu xoşbəxt bir insandır
  sentences:
  - Bu xoşbəxt bir itdir
  - Bu çox xoşbəxt bir insandır
  - Bu gün günəşli bir gündür
  example_title: Sentence Similarity
tags:
- labse
---

# TEmA-small

This model is a fine-tuned version of the [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is specialized for sentence similarity tasks in Azerbaijan texts. 
It maps sentences and paragraphs to a 768-dimensional dense vector space, useful for tasks like clustering, semantic search, and more.




## Benchmark Results

| STSBenchmark | biosses-sts | sickr-sts | sts12-sts | sts13-sts | sts15-sts | sts16-sts | Average Pearson | Model                              |
|--------------|-------------|-----------|-----------|-----------|-----------|-----------|-----------------|------------------------------------|
| 0.8253       | 0.7859      | 0.7924    | 0.8444    | 0.7490    | 0.8141    | 0.7600    | 0.7959          | TEmA-small                         |
| 0.7872       | 0.8303      | 0.7801    | 0.7978    | 0.6963    | 0.8052    | 0.7794    | 0.7823          | Cohere/embed-multilingual-v3.0     |
| 0.7927       | 0.6672      | 0.7758    | 0.8122    | 0.7312    | 0.7831    | 0.7416    | 0.7577          | BAAI/bge-m3                        |
| 0.7572       | 0.8139      | 0.7328    | 0.7646    | 0.6318    | 0.7542    | 0.7092    | 0.7377          | intfloat/multilingual-e5-large-instruct |
| 0.7400       | 0.8216      | 0.6946    | 0.7098    | 0.6781    | 0.7637    | 0.7222    | 0.7329          | labse_stripped                     |
| 0.7485       | 0.7714      | 0.7271    | 0.7170    | 0.6496    | 0.7570    | 0.7255    | 0.7280          | intfloat/multilingual-e5-large     |
| 0.7245       | 0.8237      | 0.6839    | 0.6570    | 0.7125    | 0.7612    | 0.7386    | 0.7288          | OpenAI/text-embedding-3-large       |
| 0.7363       | 0.8148      | 0.7067    | 0.7050    | 0.6535    | 0.7514    | 0.7070    | 0.7250          | sentence-transformers/LaBSE         |
| 0.7376       | 0.7917      | 0.7190    | 0.7441    | 0.6286    | 0.7461    | 0.7026    | 0.7242          | intfloat/multilingual-e5-small     |
| 0.7192       | 0.8198      | 0.7160    | 0.7338    | 0.5815    | 0.7318    | 0.6973    | 0.7142          | Cohere/embed-multilingual-light-v3.0 |
| 0.6960       | 0.8185      | 0.6950    | 0.6752    | 0.5899    | 0.7186    | 0.6790    | 0.6960          | intfloat/multilingual-e5-base       |
| 0.5830       | 0.2486      | 0.5921    | 0.5593    | 0.5559    | 0.5404    | 0.5289    | 0.5155          | antoinelouis/colbert-xm             |


[STS-Benchmark](https://github.com/LocalDoc-Azerbaijan/STS-Benchmark)




## Accuracy Results
- **Cosine Distance:** 96.63
- **Manhattan Distance:** 96.52
- **Euclidean Distance:** 96.57




## Usage

```python
from transformers import AutoTokenizer, AutoModel
import torch

# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
   token_embeddings = model_output[0] #First element of model_output contains all token embeddings
   input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
   return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Function to normalize embeddings
def normalize_embeddings(embeddings):
   return embeddings / embeddings.norm(dim=1, keepdim=True)

# Sentences we want embeddings for
sentences = [
   "Bu xoşbəxt bir insandır",
   "Bu çox xoşbəxt bir insandır", 
   "Bu gün günəşli bir gündür"
]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('LocalDoc/TEmA-small')
model = AutoModel.from_pretrained('LocalDoc/TEmA-small')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
   model_output = model(**encoded_input)

# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

# Normalize embeddings
sentence_embeddings = normalize_embeddings(sentence_embeddings)

# Calculate cosine similarities
cosine_similarities = torch.nn.functional.cosine_similarity(
   sentence_embeddings[0].unsqueeze(0), 
   sentence_embeddings[1:], 
   dim=1
)

print("Cosine Similarities:")
for i, score in enumerate(cosine_similarities):
   print(f"Sentence 1 <-> Sentence {i+2}: {score:.4f}")
```