TEmA-small

This model is a fine-tuned version of the 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
0.7252 0.7801 0.7250 0.6725 0.7446 0.7301 0.7454 0.7318 Cohere/embed-multilingual-v2.0
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

Accuracy Results

  • Cosine Distance: 96.63
  • Manhattan Distance: 96.52
  • Euclidean Distance: 96.57

Usage

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}")
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