Sbert All MiniLM L6 With Pooler

🚀 ONNX convert all-MiniLM-L6-v2

This project focuses on converting the sentence-transformers/all-MiniLM-L6-v2 model to ONNX format, enabling efficient sentence embedding and similarity tasks.

🚀 Quick Start

This is a sentence-transformers ONNX model. It maps sentences and paragraphs to a 384-dimensional dense vector space and can be used for tasks like clustering or semantic search. This custom model takes last_hidden_state and pooler_output, while the sentence-transformers exported with default ONNX config only contains last_hidden_state as output.

✨ Features

• Converts the sentence-transformers/all-MiniLM-L6-v2 model to ONNX format.
• Maps sentences and paragraphs to a 384-dimensional dense vector space.
• Can be used for tasks like clustering or semantic search.
• Custom model takes last_hidden_state and pooler_output.

📦 Installation

Using this model becomes easy when you have optimum installed:

python -m pip install optimum

💻 Usage Examples

Basic Usage

from optimum.onnxruntime.modeling_ort import ORTModelForCustomTasks
from transformers import AutoTokenizer

model = ORTModelForCustomTasks.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler")
tokenizer = AutoTokenizer.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler")
inputs = tokenizer("I love burritos!", return_tensors="pt")
pred = model(**inputs)

Advanced Usage

You will also be able to leverage the pipeline API in transformers:

from transformers import pipeline

onnx_extractor = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
text = "I love burritos!"
pred = onnx_extractor(text)

📚 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 nreimers/MiniLM-L6-H384-uncased 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.

We developed this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developed this model 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 which captures the semantic information. The sentence vector may be used for information retrieval, clustering, or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated.

Training procedure

Pre-training

We use the pretrained nreimers/MiniLM-L6-H384-uncased 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.

Hyperparameters

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.