distilroberta base

Introduction

DistilRoBERTa is a distilled version of the RoBERTa-base transformer model. It maintains similar performance levels while being more efficient, with a reduced model size of 82M parameters compared to RoBERTa-base's 125M. This model is case-sensitive and has been optimized for speed and resource efficiency.

Architecture

DistilRoBERTa consists of 6 layers, 768 hidden dimensions, and 12 attention heads, totaling 82M parameters. This architecture allows the model to be approximately twice as fast as RoBERTa-base, making it suitable for applications requiring quick processing times.

Training

The model was pre-trained on the OpenWebTextCorpus, a dataset similar to OpenAI's WebText, but with about four times less data than the original RoBERTa model. This distillation process is detailed in Hugging Face's GitHub repository.

Guide: Running Locally

1. Install Transformers Library:

   pip install transformers
   

2. Load the Model:

   from transformers import pipeline
   unmasker = pipeline('fill-mask', model='distilroberta-base')
   

3. Run Inference:

   result = unmasker("Hello I'm a <mask> model.")
   print(result)
   

4. Cloud GPUs: For enhanced performance, consider using cloud services like AWS, Google Cloud, or Azure, which offer GPU instances to accelerate the inference process.

License

DistilRoBERTa is released under the Apache 2.0 License, permitting free use, distribution, and modification of the software.