deberta v2 xlarge

Introduction

DeBERTa (Decoding-enhanced BERT with Disentangled Attention) is a model that improves upon BERT and RoBERTa by using a disentangled attention mechanism and an enhanced mask decoder. The DeBERTa V2 XLarge model comprises 24 layers and 1536 hidden units, totaling 900 million parameters. It is trained on 160GB of raw data and achieves superior performance on various natural language understanding (NLU) tasks.

Architecture

DeBERTa V2 XLarge utilizes disentangled attention, which separates content and position information for improved contextual understanding. The architecture consists of 24 transformer layers with a hidden size of 1536, making it capable of handling complex language tasks effectively.

Training

DeBERTa V2 XLarge has demonstrated high performance across a range of NLU benchmarks, including SQuAD, GLUE, and others. The model outperforms its predecessors, achieving significant improvements in tasks like SQuAD 1.1/2.0 and multiple GLUE benchmarks. Fine-tuning is recommended for specific tasks such as RTE, MRPC, and STS-B using MNLI fine-tuned models.

Guide: Running Locally

1. Setup Environment: Ensure you have Python installed along with PyTorch and Hugging Face Transformers library.

2. Clone Repository: Clone the DeBERTa repository from GitHub.

3. Install Dependencies: Use pip to install the necessary Python dependencies.

4. Download Model: Use the Transformers library to download the DeBERTa V2 XLarge model.

5. Run Training Script: Navigate to the transformers/examples/text-classification/ directory and run the following command:

   export TASK_NAME=mrpc
   python -m torch.distributed.launch --nproc_per_node=8 run_glue.py \
     --model_name_or_path microsoft/deberta-v2-xxlarge \
     --task_name $TASK_NAME \
     --do_train \
     --do_eval \
     --max_seq_length 128 \
     --per_device_train_batch_size 4 \
     --learning_rate 3e-6 \
     --num_train_epochs 3 \
     --output_dir /tmp/$TASK_NAME/ \
     --overwrite_output_dir \
     --sharded_ddp \
     --fp16
   

6. Suggested Cloud GPUs: Consider using cloud services like AWS, Google Cloud, or Azure for access to powerful GPUs, which can significantly speed up training and evaluation times.

License

The DeBERTa V2 XLarge model is licensed under the MIT License, allowing for modification and distribution with appropriate credit.