albert large chinese cluecorpussmall

Introduction

The ALBERT-LARGE-CHINESE-CLUECORPUSSMALL is a variant of ALBERT (A Lite BERT) pre-trained on Chinese text data. Developed using UER-py, this model is optimized for Chinese language tasks, leveraging the CLUECorpusSmall dataset for pre-training.

Architecture

ALBERT is designed to be efficient and lightweight, retaining the Transformer architecture while reducing memory and computational costs. The large version of ALBERT used here features 24 layers and a hidden size of 1024. It is compatible with PyTorch and TensorFlow frameworks, allowing for flexible deployment.

Training

The model was pre-trained using the UER-py toolkit on the CLUECorpusSmall dataset. Pre-training consisted of two main stages:

• Stage 1: Involved 1,000,000 steps with a sequence length of 128.
• Stage 2: Included an additional 250,000 steps with a sequence length of 512.

Both stages utilized the same hyper-parameters, with training conducted on Tencent Cloud infrastructure.

Guide: Running Locally

To use this Chinese ALBERT model:

1. Setup Environment
   Ensure Python and the Transformers library are installed. Use virtual environments to manage dependencies.

2. Install Transformers

   pip install transformers
   

3. Load the Model

   from transformers import BertTokenizer, AlbertForMaskedLM
   tokenizer = BertTokenizer.from_pretrained("uer/albert-base-chinese-cluecorpussmall")
   model = AlbertForMaskedLM.from_pretrained("uer/albert-base-chinese-cluecorpussmall")
   

4. Run Inference
   Use the model for text tasks, such as fill-mask:

   from transformers import FillMaskPipeline
   unmasker = FillMaskPipeline(model, tokenizer)
   print(unmasker("中国的首都是[MASK]京。"))
   

5. Cloud GPUs
   Consider using cloud services like AWS, GCP, or Azure for GPU resources to accelerate model inference and training tasks.

License

The model is available under the Apache License 2.0, allowing for both personal and commercial use, with proper attribution and adherence to the license terms.