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README.md

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+ # <a name="introduction"></a> ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining
+  
+ViHealthBERT is the a strong baseline language models for Vietnamese in Healthcare domain.
+
+We empirically investigate our model with different training strategies, achieving state of the art (SOTA) performances on 3 downstream tasks: NER (COVID-19 & ViMQ), Acronym Disambiguation, and Summarization.
+
+We introduce two Vietnamese datasets: the acronym dataset (acrDrAid) and the FAQ summarization dataset in the healthcare domain. Our acrDrAid dataset is annotated with 135 sets of keywords.
+The general approaches and experimental results of ViHealthBERT can be found in our LREC-2022 Poster [paper]() (updated soon):
+
+    @article{vihealthbert,
+    title     = {{ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining}},
+    author    = {Minh Phuc Nguyen, Vu Hoang Tran, Vu Hoang, Ta Duc Huy, Trung H. Bui, Steven Q. H. Truong },
+    journal   = {13th Edition of its Language Resources and Evaluation Conference},
+    year      = {2022}
+    }
+
+### Installation <a name="install2"></a>
+ -  Python 3.6+, and PyTorch >= 1.6
+ -  Install `transformers`:   
+ `pip install transformers==4.2.0` 
+     
+### Pre-trained models <a name="models2"></a>
+
+Model | #params | Arch.  | Tokenizer
+---|---|---|---
+`demdecuong/vihealthbert-base-word` | 135M | base | Word-level
+`demdecuong/vihealthbert-base-syllable` | 135M | base | Syllable-level
+
+### Example usage <a name="usage1"></a>
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+vihealthbert = AutoModel.from_pretrained("demdecuong/vihealthbert-base-word")
+tokenizer = AutoTokenizer.from_pretrained("demdecuong/vihealthbert-base-word")
+
+# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
+line = "Tôi là sinh_viên trường đại_học Công_nghệ ."
+
+input_ids = torch.tensor([tokenizer.encode(line)])
+with torch.no_grad():
+    features = vihealthbert(input_ids)  # Models outputs are now tuples
+```
+
+### Example usage for raw text <a name="usage2"></a>
+Since ViHealthBERT used the [RDRSegmenter](https://github.com/datquocnguyen/RDRsegmenter) from [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) to pre-process the pre-training data.
+We highly recommend use the same word-segmenter for ViHealthBERT downstream applications.
+
+#### Installation
+```
+# Install the vncorenlp python wrapper
+pip3 install vncorenlp
+
+# Download VnCoreNLP-1.1.1.jar & its word segmentation component (i.e. RDRSegmenter) 
+mkdir -p vncorenlp/models/wordsegmenter
+wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/VnCoreNLP-1.1.1.jar
+wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/vi-vocab
+wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/wordsegmenter.rdr
+mv VnCoreNLP-1.1.1.jar vncorenlp/ 
+mv vi-vocab vncorenlp/models/wordsegmenter/
+mv wordsegmenter.rdr vncorenlp/models/wordsegmenter/
+```
+
+`VnCoreNLP-1.1.1.jar` (27MB) and folder `models/` must be placed in the same working folder.
+
+#### Example usage
+```
+# See more details at: https://github.com/vncorenlp/VnCoreNLP
+
+# Load rdrsegmenter from VnCoreNLP
+from vncorenlp import VnCoreNLP
+rdrsegmenter = VnCoreNLP("/Absolute-path-to/vncorenlp/VnCoreNLP-1.1.1.jar", annotators="wseg", max_heap_size='-Xmx500m') 
+
+# Input 
+text = "Ông Nguyễn Khắc Chúc  đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."
+
+# To perform word (and sentence) segmentation
+sentences = rdrsegmenter.tokenize(text) 
+for sentence in sentences:
+	print(" ".join(sentence))
+```

config.json

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+{
+  "_name_or_path": "/vinbrain/minhnp/HnBERT/repo/ViHNews-BERT/pretraining/ckpt_1.1.syllabel",
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "finetuning_task": "word-level",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 258,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "PhobertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.11.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 64001
+}

pytorch_model.bin

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