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Add DeBERTa XXLarge(1.5B) model fine-tuned with MNLI task

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  1. README.md +55 -0
  2. config.json +29 -0
  3. pytorch_model.bin +3 -0
  4. spm.model +3 -0
  5. tokenizer_config.json +4 -0
README.md ADDED
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+ ---
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+ thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
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+ license: mit
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+ ---
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+
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+ ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
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+
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+ [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
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+
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+ Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
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+
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+ This the DeBERTa V2 XXLarge model fine-tuned with MNLI task, 48 layers, 1536 hidden size. Total parameters 1.5B.
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+
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+
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+ ### Fine-tuning on NLU tasks
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+
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+ We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks.
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+
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+ | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B |
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+ |---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
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+ | | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S |
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+ | 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/- |
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+ | 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/- |
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+ | 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/- |
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+ | [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)<sup>1</sup> | 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 |
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+ | [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)<sup>1</sup> | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7|
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+ | [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)<sup>1</sup>|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|
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+ |**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)<sup>1,2</sup>**|**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** |
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+ --------
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+ #### Notes.
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+ - <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.
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+ - <sup>2</sup> To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, you need to specify **--sharded_ddp**
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+
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+ ```bash
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+ cd transformers/examples/text-classification/
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+ export TASK_NAME=mrpc
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+ python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \
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+ --task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 4 \
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+ --learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16
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+ ```
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+
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+ ### Citation
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+
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+ If you find DeBERTa useful for your work, please cite the following paper:
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+
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+ ``` latex
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+ @inproceedings{
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+ he2021deberta,
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+ title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
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+ author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
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+ booktitle={International Conference on Learning Representations},
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+ year={2021},
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+ url={https://openreview.net/forum?id=XPZIaotutsD}
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+ }
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+ ```
config.json ADDED
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+ {
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+ "model_type": "deberta-v2",
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+ "attention_probs_dropout_prob": 0.1,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "hidden_size": 1536,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 6144,
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+ "max_position_embeddings": 512,
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+ "relative_attention": true,
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+ "position_buckets": 256,
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+ "norm_rel_ebd": "layer_norm",
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+ "share_att_key": true,
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+ "pos_att_type": "p2c|c2p",
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+ "layer_norm_eps": 1e-7,
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+ "conv_kernel_size": 3,
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+ "conv_act": "gelu",
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+ "max_relative_positions": -1,
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+ "position_biased_input": false,
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+ "num_attention_heads": 24,
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+ "attention_head_size": 64,
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+ "num_hidden_layers": 48,
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+ "type_vocab_size": 0,
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+ "vocab_size": 128100,
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+ "pooling": {
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+ "dropout": 0,
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+ "hidden_act": "gelu"
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+ }
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+ }
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+ {
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+ "do_lower_case": false,
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+ "vocab_type": "spm"
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+ }