Edit model card

momen-text-embed-v1

momen-text-embed-v1 maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. it surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks in arabic language.

Performance Benchmarks

Soon

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ['هذه جملة لغرض التجربة', 'جملة باللغة العربية ليتم تحويلها']

model = SentenceTransformer('ALJIACHI/momen-text-embed-v1')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F

#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)


# Sentences we want sentence embeddings for
sentences = ['هذه جملة لغرض التجربة', 'جملة باللغة العربية ليتم تحويلها']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ALJIACHI/momen-text-embed-v1')
model = AutoModel.from_pretrained('ALJIACHI/momen-text-embed-v1')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, 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 = F.normalize(sentence_embeddings, p=2, dim=1)

print("Sentence embeddings:")
print(sentence_embeddings)
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.