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README.md

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+---
+language:
+- vi
+metrics:
+- f1
+pipeline_tag: token-classification
+tags:
+- transformer
+- vietnamese
+- nlp
+- bert
+- deberta
+- deberta-v3
+---
+
+# ViDeBERTa: A powerful pre-trained language model for Vietnamese
+
+
+ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, 
+with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, 
+which are pre-trained on 138GB of Vietnamese text of high-quality and diverse Vietnamese text using DeBERTaV3 architecture. 
+
+Please check the [official repository][github] for more implementation details and updates
+
+The DeBERTa V3 xsmall model comes with 12 layers and a hidden size 
+of 384. It has only 22M backbone parameters with a vocabulary 
+containing 128K tokens which introduces 48M parameters in the 
+Embedding layer. This model was trained using CC100 dataset, which consists of 138 GB of Vietnamese text.
+
+## Fine-tuning on NLU tasks
+We present the dev results on VLSP POS, PhoNER, ViQuAD dataset.
+
+| Model|#Params(M)| POS | NER | MRC |
+|-----------|-------|---------|-----|----------|
+| XLM-R-base      | 125M  | 96.2 | - | 82.0 |
+| XLM-R-large  | 355M | 96.3 | 93.8 | 87.0 |
+| PhoBERT-base | 135M | 96.7 | 80.1 |
+| PhoBERT-large | 370M | 96.8 | 83.5 |
+| ViT5-base | 310M | - | 94.5 | - |
+| ViT5-large | 866M | - | 93.8 | - |
+| **ViDeBERTa-xsmall** | **22M** | **96.4** | **93.6** | **81.3** |
+| ViDeBERTa-base | 86M | 96.8 | 94.5 | 85.7 |
+| ViDeBERTa-large | 304M | 97.2 | 95.3 | 89.9 |
+
+## Citation
+If you find ViDeBERTa useful for your work, please cite the following papers:
+```latex
+@article{dao2023videberta,
+  title={ViDeBERTa: A powerful pre-trained language model for Vietnamese},
+  author={Dao Tran, Cong and Pham, Nhut Huy and Nguyen, Anh and Son Hy, Truong and Vu, Tu},
+  journal={arXiv e-prints},
+  pages={arXiv--2301},
+  year={2023}
+}
+```
+
+[github]: https://github.com/HySonLab/ViDeBERTa

config.json

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+{
+  "attention_head_size": 64,
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pos_att_type": "p2c|c2p",
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "type_vocab_size": 0,
+  "vocab_size": 128000
+}
+

pytorch_model.bin

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+oid sha256:535b69f2c722ea7dc0415bded1044861cba90ad0962863167fadbff2cc394ecc
+size 566908530

special_tokens_map.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "bos_token": "[CLS]",
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "mask_token": "[MASK]",
+  "name_or_path": "microsoft/deberta-v3-xsmall",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "special_tokens_map_file": null,
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}