Update README.md

README.md

@@ -9,6 +9,11 @@ thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
 license: mit
 pipeline_tag: zero-shot-classification
 ---
+## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
+[DeBERTa](https://arxiv.org/abs/2006.03654) 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](https://github.com/microsoft/DeBERTa) for more details and updates.
+This is the DeBERTa V2 xxlarge model with 48 layers, 1536 hidden size. The total parameters are 1.5B and it is trained with 160GB raw data.
+### 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 |
 |---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
@@ -24,4 +29,50 @@ We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
 #### Notes.
  - <sup>1</sup> 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 start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
  - <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, we recommand using **deepspeed** as it's faster and saves memory.
- 
+ 
+Run with `Deepspeed`,
+```bash
+pip install datasets
+pip install deepspeed
+# Download the deepspeed config file
+wget https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/ds_config.json -O ds_config.json
+export TASK_NAME=mnli
+output_dir="ds_results"
+num_gpus=8
+batch_size=8
+python -m torch.distributed.launch --nproc_per_node=${num_gpus} \\
+  run_glue.py \\
+  --model_name_or_path microsoft/deberta-v2-xxlarge \\
+  --task_name $TASK_NAME \\
+  --do_train \\
+  --do_eval \\
+  --max_seq_length 256 \\
+  --per_device_train_batch_size ${batch_size} \\
+  --learning_rate 3e-6 \\
+  --num_train_epochs 3 \\
+  --output_dir $output_dir \\
+  --overwrite_output_dir \\
+  --logging_steps 10 \\
+  --logging_dir $output_dir \\
+  --deepspeed ds_config.json
+```
+You can also run with `--sharded_ddp`
+```bash  
+cd transformers/examples/text-classification/
+export TASK_NAME=mnli
+python -m torch.distributed.launch --nproc_per_node=8 run_glue.py   --model_name_or_path microsoft/deberta-v2-xxlarge   \\
+--task_name $TASK_NAME   --do_train   --do_eval   --max_seq_length 256   --per_device_train_batch_size 8   \\
+--learning_rate 3e-6   --num_train_epochs 3   --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
+```
+### Citation
+If you find DeBERTa useful for your work, please cite the following paper:
+``` latex
+@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}
+}
+```