README.md · NDugar/ZSD-microsoft-v2xxlmnli at cb49e14c290169bf1e3cf4f9f8c0df2a87ca7fac

metadata

language: en
tags:
  - deberta-v1
  - deberta-mnli
tasks: mnli
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
pipeline_tag: zero-shot-classification

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 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 MNLI task. #### Fine-tuning on NLU tasks We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks. | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B | |---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------| | | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S | | BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- | | RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- | | XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- | | DeBERTa-Large¹ | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 | | DeBERTa-XLarge¹ | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7| | DeBERTa-V2-XLarge¹|95.8/90.8| 91.4/88.9|91.7/91.6| 97.5| 95.8|71.1|93.9|92.0/94.2|92.3/89.8|92.9/92.9| |DeBERTa-V2-XXLarge^1,2|96.1/91.4|92.2/89.7|91.7/91.9|97.2|96.0|72.0| 93.5| 93.1/94.9|92.7/90.3 |93.2/93.1 |

Notes.

¹ Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI. The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start 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

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   \\\n--task_name $TASK_NAME   --do_train   --do_eval   --max_seq_length 128   --per_device_train_batch_size 4   \\\n--learning_rate 3e-6   --num_train_epochs 3   --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16

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}
}