Duplicate from microsoft/deberta-v2-xxlarge

Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+language: en
+tags: 
+- deberta
+- fill-mask
+thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
+license: mit
+---
+
+## 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 |
+|---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------|
+|                           | 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-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 |
+| [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|
+| [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|
+|**[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** |
+--------
+#### 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}
+}
+```

config.json

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+{
+	"model_type": "deberta-v2",
+	"attention_probs_dropout_prob": 0.1,
+	"hidden_act": "gelu",
+	"hidden_dropout_prob": 0.1,
+	"hidden_size": 1536,
+	"initializer_range": 0.02,
+	"intermediate_size": 6144,
+	"max_position_embeddings": 512,
+	"relative_attention": true,
+	"position_buckets": 256,
+	"norm_rel_ebd": "layer_norm",
+	"share_att_key": true,
+	"pos_att_type": "p2c|c2p",
+	"layer_norm_eps": 1e-7,
+	"conv_kernel_size": 3,
+	"conv_act": "gelu",
+	"max_relative_positions": -1,
+	"position_biased_input": false,
+	"num_attention_heads": 24,
+	"attention_head_size": 64,
+	"num_hidden_layers": 48,
+	"type_vocab_size": 0,
+	"vocab_size": 128100
+}

ds_config.json

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+{
+  "fp16": {
+
+    "enabled": true,
+
+    "initial_scale_power": 12
+
+  },
+  "zero_optimization": {
+
+    "stage": 2,
+
+    "reduce_bucket_size": 5e7,
+
+    "allgather_bucket_size": 1.25e9,
+
+    "overlap_comm": true,
+
+    "contiguous_gradients": true
+
+  },
+  "zero_allow_untested_optimizer": true
+}

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tokenizer_config.json

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+{
+  "do_lower_case": false,
+  "vocab_type": "spm"
+}