cde small v1

Introduction

The cde-small-v1 is a model hosted on Hugging Face designed for feature extraction and compatible with libraries such as sentence-transformers, safetensors, and transformers. It has applications in tasks that require efficient and accurate feature extraction from text data.

Architecture

The model leverages the sentence-transformers framework, which is optimized for creating sentence and text embeddings. It is designed to be lightweight and efficient, suitable for deployment in various environments. The architecture allows for integration with other machine learning frameworks and supports easy customization for different use cases.

Training

The model was trained using a specific dataset and optimized for performance in feature extraction tasks. The training process included fine-tuning parameters to balance speed and accuracy. The training data and methodology were selected to ensure the model's robustness and adaptability to different text analysis scenarios.

Guide: Running Locally

1. Clone the Repository:
   Access the model's repository on Hugging Face and clone it to your local machine.

   git clone https://huggingface.co/jxm/cde-small-v1
   

2. Install Dependencies:
   Ensure you have the necessary libraries installed, including transformers and sentence-transformers.

   pip install transformers sentence-transformers
   

3. Load the Model:
   Utilize the transformers library to load the model for inference.

   from transformers import AutoModel, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("jxm/cde-small-v1")
   model = AutoModel.from_pretrained("jxm/cde-small-v1")
   

4. Inference:
   Use the model to extract features from your text data.

   inputs = tokenizer("Your text here", return_tensors="pt")
   outputs = model(**inputs)
   

Cloud GPU Suggestion:
For enhanced performance, consider using cloud services such as AWS EC2, Google Cloud AI Platform, or Azure Machine Learning with GPU instances to run the model.

License

The model is subject to specific licensing terms available on its Hugging Face repository. Users must comply with these terms when using, modifying, or distributing the model and its derivatives.