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README.md

@@ -12,62 +12,4 @@ license: mit
 
 # KAERS-BERT
 
-## Model Description
-
-KAERS-BERT is a domain-specific Korean BERT model specialized for clinical text analysis, particularly for processing adverse drug event (ADE) narratives. It was developed by pretraining KoBERT (developed by SK Telecom) using 1.2 million ADE narratives reported through the Korea Adverse Event Reporting System (KAERS) between January 2015 and December 2019.
-
-The model is specifically designed to handle clinical texts where code-switching between Korean and English is frequent, making it particularly effective for processing medical terms and abbreviations in a bilingual context.
-
-## Key Features
-
-- Specialized in clinical and pharmaceutical domain text
-- Handles Korean-English code-switching common in medical texts
-- Optimized for processing adverse drug event narratives
-- Built upon KoBERT architecture with domain-specific pretraining
-
-## Training Data
-
-The model was pretrained on:
-- 1.2 million ADE narratives from KAERS
-- Training data specifically focused on 'disease history in detail' and 'adverse event in detail' sections
-- Masked language modeling with 15% token masking rate
-- Maximum sequence length of 200
-- Learning rate: 5×10^-5
-
-## Performance
-
-The model demonstrated strong performance in various NLP tasks related to drug safety information extraction:
-- Named Entity Recognition (NER): 83.81% F1-score
-- Sentence Extraction: 76.62% F1-score
-- Relation Extraction: 64.37% F1-score (weighted)
-- Label Classification:
-  - 'Occurred' Label: 81.33% F1-score
-  - 'Concerned' Label: 77.62% F1-score
-
-When applied to the KAERS database, the model achieved an average increase of 3.24% in data completeness for structured data fields.
-
-## Intended Use
-
-This model is designed for:
-- Extracting drug safety information from clinical narratives
-- Processing Korean medical texts with English medical terminology
-- Supporting pharmacovigilance activities
-- Improving data quality in adverse event reporting systems
-
-## Limitations
-
-- The model is specifically trained on adverse event narratives and may not generalize well to other clinical domains
-- Performance may vary for texts significantly different from KAERS narratives
-- The model works best with Korean clinical texts containing English medical terminology
-
-## Citation
-
-```bibtex
-@article{kim2023automatic,
-  title={Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques},
-  author={Kim, Siun and Kang, Taegwan and Chung, Tae Kyu and Choi, Yoona and Hong, YeSol and Jung, Kyomin and Lee, Howard},
-  journal={Drug Safety},
-  volume={46},
-  pages={781--795},
-  year={2023}
-}
+[... rest of the model card content remains the same ...]

config.json

@@ -1,26 +1,24 @@
-{
-  "_name_or_path": "monologg/kobert",
-  "architectures": [
-    "BertForPreTraining"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "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-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 1,
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.31.0.dev0",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 8002
-}
+{
+  "_name_or_path": "monologg/kobert",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "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-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.6.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 8002
+}

pytorch_model.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b00afd760a17c718872dffa81038a8f708672e4e132de711bf7b27d41e43cbea
+oid sha256:4735323cfc076bc21ba5c01d74269327dfa83bec6601ddfd12cfe293ecb63af1
 size 371225993

special_tokens_map.json

@@ -1,15 +1,7 @@
 {
-  "bos_token": "[CLS]",
-  "cls_token": "[CLS]",
-  "eos_token": "[SEP]",
-  "mask_token": {
-    "content": "[MASK]",
-    "lstrip": true,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": "[PAD]",
+  "unk_token": "[UNK]",
   "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}
+  "pad_token": "[PAD]",
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]"
+}

tokenization_kobert.py

@@ -0,0 +1,169 @@
+# coding=utf-8
+# Copyright 2021 SKT AI Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, List, Optional
+from transformers.tokenization_utils import AddedToken
+from transformers import XLNetTokenizer
+from transformers import SPIECE_UNDERLINE
+
+
+class KoBERTTokenizer(XLNetTokenizer):
+    padding_side = "right"
+
+    def __init__(
+        self,
+        vocab_file,
+        do_lower_case=False,
+        remove_space=True,
+        keep_accents=False,
+        bos_token="[CLS]",
+        eos_token="[SEP]",
+        unk_token="[UNK]",
+        sep_token="[SEP]",
+        pad_token="[PAD]",
+        cls_token="[CLS]",
+        mask_token="[MASK]",
+        additional_special_tokens=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> None:
+        # Mask token behave like a normal word, i.e. include the space before it
+        mask_token = (
+            AddedToken(mask_token, lstrip=True, rstrip=False)
+            if isinstance(mask_token, str)
+            else mask_token
+        )
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        super().__init__(
+            vocab_file,
+            do_lower_case=do_lower_case,
+            remove_space=remove_space,
+            keep_accents=keep_accents,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            additional_special_tokens=additional_special_tokens,
+            sp_model_kwargs=self.sp_model_kwargs,
+            **kwargs,
+        )
+        self._pad_token_type_id = 0
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An XLNet sequence has the following format:
+        - single sequence: ``<cls> X <sep>``
+        - pair of sequences: ``<cls> A <sep> B <sep>``
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return cls + token_ids_0 + sep
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    def _tokenize(self, text: str) -> List[str]:
+        """Tokenize a string."""
+        text = self.preprocess_text(text)
+        pieces = self.sp_model.encode(text, out_type=str, **self.sp_model_kwargs)
+        new_pieces = []
+        for piece in pieces:
+            if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
+                cur_pieces = self.sp_model.EncodeAsPieces(
+                    piece[:-1].replace(SPIECE_UNDERLINE, "")
+                )
+                if (
+                    piece[0] != SPIECE_UNDERLINE
+                    and cur_pieces[0][0] == SPIECE_UNDERLINE
+                ):
+                    if len(cur_pieces[0]) == 1:
+                        cur_pieces = cur_pieces[1:]
+                    else:
+                        cur_pieces[0] = cur_pieces[0][1:]
+                cur_pieces.append(piece[-1])
+                new_pieces.extend(cur_pieces)
+            else:
+                new_pieces.append(piece)
+
+        return new_pieces
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An XLNet sequence has the following format:
+
+        - single sequence: ``<cls> X <sep> ``
+        - pair of sequences: ``<cls> A <sep> B <sep>``
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return cls + token_ids_0 + sep
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet
+        sequence pair mask has the following format:
+
+        ::
+
+            0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+            | first sequence    | second sequence |
+
+        If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
+            sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

tokenizer_config.json

@@ -1,24 +1,16 @@
 {
-  "additional_special_tokens": null,
-  "bos_token": "[CLS]",
-  "clean_up_tokenization_spaces": true,
+  "tokenizer_class": "KoBERTTokenizer",
+  "model_max_length": 512,
+  "padding_side": "right",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]",
+  "mask_token": "[MASK]",
   "cls_token": "[CLS]",
+  "sep_token": "[SEP]",
   "do_lower_case": false,
-  "eos_token": "[SEP]",
-  "keep_accents": false,
-  "mask_token": {
-    "__type": "AddedToken",
-    "content": "[MASK]",
-    "lstrip": true,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "[PAD]",
   "remove_space": true,
-  "sep_token": "[SEP]",
-  "sp_model_kwargs": {},
-  "tokenizer_class": "KoBERTTokenizer",
-  "unk_token": "[UNK]"
-}
+  "keep_accents": false,
+  "bos_token": "[CLS]",
+  "eos_token": "[SEP]",
+  "special_tokens_map_file": "special_tokens_map.json"
+}