Deberta Large Mnli Zero Cls

🚀 DeBERTa: Decoding-enhanced BERT with Disentangled Attention

DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on the majority of NLU tasks with 80GB training data.

Please check the official repository for more details and updates. This is the DeBERTa large model fine - tuned with the MNLI task.

🚀 Quick Start

This is a quick overview of the DeBERTa model and its fine - tuning results. For more in - depth information, refer to the official repository and the citation paper.

✨ Features

• Enhanced Architecture: DeBERTa improves upon BERT and RoBERTa using disentangled attention and an enhanced mask decoder.
• High Performance: It outperforms BERT and RoBERTa on the majority of NLU tasks with 80GB training data.

📚 Documentation

Fine - tuning on NLU tasks

We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.

Notes.

• ¹ Following RoBERTa, for RTE, MRPC, STS - B, we fine - tune the tasks based on [DeBERTa - Large - MNLI](https://huggingface.co/microsoft/deberta - large - mnli), [DeBERTa - XLarge - MNLI](https://huggingface.co/microsoft/deberta - xlarge - mnli), [DeBERTa - V2 - XLarge - MNLI](https://huggingface.co/microsoft/deberta - v2 - xlarge - mnli), [DeBERTa - V2 - XXLarge - MNLI](https://huggingface.co/microsoft/deberta - v2 - xxlarge - mnli). The results of SST - 2/QQP/QNLI/SQuADv2 will also be slightly improved when starting from MNLI fine - tuned models. However, we only report the numbers fine - tuned from pretrained base models for those 4 tasks.
• ² To try the XXLarge model with HF transformers, you need to specify --sharded_ddp

💻 Usage Examples

Basic Usage

cd transformers/examples/text - classification/
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

📄 License

This project is licensed under the MIT license.

Citation

If you find DeBERTa useful for your work, please cite the following paper:

@inproceedings{
he2021deberta,
title={DEBERTA: DECODING - ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}