Laprador Pt Pb

🚀 gemasphi/laprador_pt

This model, based on sentence-transformers, maps sentences and paragraphs into a 768-dimensional dense vector space. It's applicable for tasks such as clustering and semantic search.

🚀 Quick Start

This section provides two ways to use the gemasphi/laprador_pt model, with and without the sentence-transformers library.

✨ Features

• Maps sentences and paragraphs to a 768-dimensional dense vector space.
• Suitable for clustering and semantic search tasks.

📦 Installation

To use this model, you first need to install the sentence-transformers library. You can do this by running the following command:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

If you have the sentence-transformers library installed, using the model is straightforward:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('gemasphi/laprador_pt')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage

Without the sentence-transformers library, you can use the model as follows. First, pass your input through the transformer model, and then apply the appropriate pooling operation on 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 = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('gemasphi/laprador_pt')
model = AutoModel.from_pretrained('gemasphi/laprador_pt')

# 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)

📚 Documentation

Evaluation Results

For an automated evaluation of this model, refer to the Sentence Embeddings Benchmark: https://seb.sbert.net

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

📄 License

The README does not provide license information.