Update README.md

README.md

@@ -41,64 +41,6 @@ It achieves the following results on the evaluation set:
 Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
 In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we replaced the MLM objective with the RTD(Replaced Token Detection) objective introduced by ELECTRA for pre-training, as well as some innovations to be introduced in our upcoming paper. Compared to DeBERTa-V2,  our V3 version significantly improves the model performance in downstream tasks.  You can find a simple introduction about the model from the appendix A11 in our original [paper](https://arxiv.org/abs/2006.03654),  but we will provide more details in a separate write-up.
 The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. Its total parameter number is 143M since we use a vocabulary containing 128K tokens which introduce 98M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.
-#### Fine-tuning on NLU tasks
-We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
-| Model             | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
-|-------------------|-----------|-----------|--------|
-| RoBERTa-base      | 91.5/84.6 | 83.7/80.5 | 87.6   |
-| XLNet-base        | -/-       | -/80.2    | 86.8   |
-|DeBERTa-base	|93.1/87.2|	86.2/83.1|	88.8|
-| **DeBERTa-v3-small**  | -/- | -/- | 88.2   |
-| DeBERTa-v3-small+SiFT  | -/- | -/- | 88.8   |
-#### Fine-tuning with HF transformers
-```bash
-#!/bin/bash
-cd transformers/examples/pytorch/text-classification/
-pip install datasets
-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-v3-small \
-  --task_name $TASK_NAME \
-  --do_train \
-  --do_eval \
-  --evaluation_strategy steps \
-  --max_seq_length 256 \
-  --warmup_steps 1000 \
-  --per_device_train_batch_size ${batch_size} \
-  --learning_rate 3e-5 \
-  --num_train_epochs 3 \
-  --output_dir $output_dir \
-  --overwrite_output_dir \
-  --logging_steps 1000 \
-  --logging_dir $output_dir
-```
-### Citation
-If you find DeBERTa useful for your work, please cite the following paper:
-``` latex
-@misc{he2021debertav3,
-      title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, 
-      author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
-      year={2021},
-      eprint={2111.09543},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
-``` 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}
-}
-```
-
 
 ## Intended uses & limitations