Sentence It5 Small

🚀 sentence-IT5-small

This is a sentence-transformers model that maps sentences and paragraphs to a 512-dimensional dense vector space. It can be used for tasks such as clustering or semantic search. It is a T5 (IT5) small model trained for asymmetric semantic search, where the query is a keyword and the paragraph is a short news article.

🚀 Quick Start

📦 Installation

Using this model becomes easy when you have sentence-transformers installed. You can install it with the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage (Sentence-Transformers)

After installation, you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]

model = SentenceTransformer('efederici/sentence-IT5-small')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model by passing your input through the transformer model and then applying the right pooling - operation on top of the contextualized word embeddings.

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)


# Sentences we want sentence embeddings for
sentences = ["Questo è un esempio di frase", "Questo è un ulteriore esempio"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('efederici/sentence-IT5-small')
model = AutoModel.from_pretrained('efederici/sentence-IT5-small')

# 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. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

🔧 Technical Details

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': None, 'do_lower_case': False}) with Transformer model: T5EncoderModel 
  (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Information Table