All Mpnet Base V2 Feature Extraction Pipeline

🚀 all-mpnet-base-v2

This is a sentence-transformers model that maps sentences & paragraphs to a 768-dimensional dense vector space, useful for tasks like clustering or semantic search.

🚀 Quick Start

This model can be used easily in two ways, depending on whether you have sentence-transformers installed.

✨ Features

• Maps sentences and paragraphs to a 768-dimensional dense vector space.
• Can be used for clustering, semantic search, and other related tasks.

📦 Installation

If you want to use the sentence-transformers way, you need to install it first:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage (Sentence-Transformers)

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

model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)

Advanced Usage (HuggingFace Transformers)

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 = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')

# 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)

📚 Documentation

Evaluation Results

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

Background

The project aims to train sentence embedding models on very large sentence-level datasets using a self-supervised contrastive learning objective. We used the pretrained microsoft/mpnet-base model and fine-tuned it on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences was actually paired with it in our dataset.

This model was developed during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. It was developed as part of the project Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3 - 8, as well as intervention from Google's Flax, JAX, and Cloud team members about efficient deep learning frameworks.

Intended uses

Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector that captures the semantic information. The sentence vector may be used for information retrieval, clustering, or sentence similarity tasks. By default, input text longer than 384 word pieces is truncated.

Training procedure

Pre - training

We use the pretrained microsoft/mpnet-base model. Please refer to the model card for more detailed information about the pre - training procedure.

Fine - tuning

We fine - tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pair from the batch. We then apply the cross - entropy loss by comparing with true pairs.

Hyper parameters

We trained our model on a TPU v3 - 8. We trained the model for 100k steps using a batch size of 1024 (128 per TPU core). We used a learning rate warm - up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e - 5 learning rate. The full training script is accessible in this current repository: train_script.py.

Training data

We use the concatenation of multiple datasets to fine - tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability, and the configuration is detailed in the data_config.json file.

📄 License

This project is licensed under the Apache 2.0 license.