Model Card for Fine-tuned sentence-transformers/all-MiniLM-L6-v2 for RAG Application

Model Details

• Model Name: Fine-tuned sentence-transformers/all-MiniLM-L6-v2
• Model Type: Sentence Embedding Model
• Architecture: Transformer-based, MiniLM
• Language: English
• License: Apache-2.0
• Model Version: 1.0
• Hugging Face Organization: sentence-transformers
• Authors: The team behind Sentence Transformers and contributors.

Intended Use

• Primary Applications: This model is fine-tuned specifically for Retrieval-Augmented Generation (RAG) applications, aiming to improve semantic retrieval capabilities in question-answering, chatbots, and information retrieval systems.
• Intended Users: Researchers, developers, and enterprises looking to enhance their NLP applications with advanced semantic search capabilities.
• Out-of-Scope Use Cases: Not intended for real-time, low-latency applications due to the computational requirements of transformer models.

Training Data

The fine-tuning process utilized a curated dataset compiled from diverse sources including academic papers, web text, and domain-specific datasets to ensure a broad semantic understanding. The data was preprocessed to focus on sentence and paragraph-level embeddings that are relevant for semantic retrieval tasks.

Training Procedure

• Preprocessing: Texts were cleaned, tokenized, and encoded using the pre-trained sentence-transformers/all-MiniLM-L6-v2 tokenizer.
• Fine-tuning: The model was fine-tuned on a task-specific dataset using a contrastive loss function that minimizes the distance between semantically similar sentences and maximizes it between dissimilar ones.
• Epochs: Training was conducted for 10 epochs with early stopping based on validation loss to prevent overfitting.
• Optimizer: AdamW with a learning rate of 2e-5.

Evaluation Results

The fine-tuned model demonstrates superior performance in semantic similarity and retrieval tasks compared to the base all-MiniLM-L6-v2 model, with significant improvements in Precision@k, Recall@k, and F1 scores on benchmark datasets.

Ethical Considerations

This model is built on data sources that may contain biases. Users should be aware of potential biases when deploying the model in diverse contexts and strive to mitigate such biases when possible.

Caveats and Recommendations

• Computational Resources: Running the model, especially in RAG setups, requires substantial computational resources. Users should consider this when integrating the model into their applications.
• Domain-specific Adaptations: While the model performs well across a broad range of topics, fine-tuning on domain-specific data is recommended for optimal performance in specialized applications.

How to Use

Here is a simple example of how to use this model in your application:

from sentence_transformers import SentenceTransformer

# Load the fine-tuned model
model = SentenceTransformer('your-model-path/sentence-transformers-all-MiniLM-L6-v2-finetuned')

# Encode sentences to get their embeddings
sentences = ["What is the capital of France?", "Tell me about the Eiffel tower."]
embeddings = model.encode(sentences)

# Use embeddings for semantic search, clustering, or other applications

For more detailed instructions, including how to integrate the model with RAG setups for semantic retrieval, please refer to the official documentation and examples provided by Sentence Transformers and Hugging Face.

Acknowledgments

This work builds upon the efforts of the Sentence Transformers and Hugging Face teams, as well as the broader NLP community. We thank all contributors for their invaluable work in advancing the field of natural language processing.

References

• Original sentence-transformers/all-MiniLM-L6-v2 model: Hugging Face Model Hub
• Sentence Transformers Documentation: SBERT.net
• Hugging Face Transformers: Transformers Library

License

This model is open-sourced under the Apache 2.0 license. The full license can be found in the LICENSE file in the model's repository.