Add models

README.md

@@ -1,3 +1,128 @@
 ---
+language: ja
+tags:
+- ja
+- japanese
+- bart
+- lm
+- nlp
 license: mit
 ---
+
+# bart-base-japanese-news(base-sized model)
+This repository provides a Japanese BART model. The model was trained by [Stockmark Inc.](https://stockmark.co.jp)
+
+
+## Model description
+
+BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
+
+BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering).
+
+## Intended uses & limitations
+
+You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset.
+
+# How to use the model
+
+*NOTE:* Use `trust_remote_code=True` to initiate the tokenizer.
+
+## Simple use
+
+```python
+from transformers import AutoTokenizer, BartModel
+
+model_name = "stockmark/bart-base-japanese-news"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = BartModel.from_pretrained(model_name)
+
+inputs = tokenizer("今日は良い天気です。", return_tensors="pt")
+outputs = model(**inputs)
+
+last_hidden_states = outputs.last_hidden_state
+```
+
+## Sentence Permutation
+```python
+import torch
+from transformers import AutoTokenizer, BartForConditionalGeneration
+
+model_name = "stockmark/bart-base-japanese-news"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+if torch.cuda.is_available():
+    model = model.to("cuda")
+
+# correct order text is "明日は大雨です。電車は止まる可能性があります。ですから、自宅から働きます。"
+text = "電車は止まる可能性があります。ですから、自宅から働きます。明日は大雨です。"
+
+inputs = tokenizer([text], max_length=128, return_tensors="pt", truncation=True)
+text_ids = model.generate(inputs["input_ids"].to(model.device), num_beams=3, max_length=128)
+output = tokenizer.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+print(output)
+# sample output: 明日は大雨です。電車は止まる可能性があります。ですから、自宅から働きます。
+```
+## Mask filing
+```python
+import torch
+from transformers import AutoTokenizer, BartForConditionalGeneration
+
+model_name = "stockmark/bart-base-japanese-news"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+if torch.cuda.is_available():
+    model = model.to("cuda")
+
+text = "今日の天気は<mask>のため、傘が必要でしょう。"
+
+inputs = tokenizer([text], max_length=128, return_tensors="pt", truncation=True)
+text_ids = model.generate(inputs["input_ids"].to(model.device), num_beams=3, max_length=128)
+output = tokenizer.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+print(output)
+# sample output: 今日の天気は、雨のため、傘が必要でしょう。
+```
+
+## Text generation
+
+*NOTE:* You can use the raw model for text generation. However, the model is mostly meant to be fine-tuned on a supervised dataset.
+
+```python
+import torch
+from transformers import AutoTokenizer, BartForConditionalGeneration
+
+model_name = "stockmark/bart-base-japanese-news"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+if torch.cuda.is_available():
+   model = model.to("cuda")
+
+text = "自然言語処理（しぜんげんごしょり、略称：NLP）は、人間が日常的に使っている自然言語をコンピュータに処理させる一連の技術であり、人工知能と言語学の一分野である。「計算言語学」（computational linguistics）との類似もあるが、自然言語処理は工学的な視点からの言語処理をさすのに対して、計算言語学は言語学的視点を重視する手法をさす事が多い。"
+
+inputs = tokenizer([text], max_length=512, return_tensors="pt", truncation=True)
+text_ids = model.generate(inputs["input_ids"].to(model.device), num_beams=3, min_length=0, max_length=40)
+output = tokenizer.batch_decode(text_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+print(output)
+# sample output: 自然言語処理(しぜんげんごしょり、略称:NLP)は、人間が日常的に使っている自然言語をコンピュータに処理させる一連の技術であり、言語学の一分野である。
+```
+
+# Training
+The model was trained on Japanese News Articles.
+
+# Tokenization
+The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer. The vocabulary was first trained on a selected subset from the training data using the official sentencepiece training script.
+
+# Licenses
+The pretrained models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php).
+
+# Acknowledgement
+This comparison study supported with Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).

config.json

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+{
+  "_name_or_path": "bart-base-japanese-news",
+  "activation_dropout": 0.1,
+  "activation_function": "gelu",
+  "architectures": [
+    "BartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_dropout": 0.0,
+  "d_model": 768,
+  "decoder_attention_heads": 12,
+  "decoder_ffn_dim": 3072,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 6,
+  "decoder_start_token_id": 1,
+  "dropout": 0.1,
+  "encoder_attention_heads": 12,
+  "encoder_ffn_dim": 3072,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 6,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_position_embeddings": 512,
+  "model_type": "bart",
+  "num_hidden_layers": 6,
+  "pad_token_id": 3,
+  "scale_embedding": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.3",
+  "use_cache": false,
+  "vocab_size": 32000
+}

pytorch_model.bin

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+size 498636537

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

spiece.model

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+size 828378

tf_model.h5

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+oid sha256:f2c1df518fdd0baa436241ec54e81c21eef9af3a55d5de6af86a7f6d9d6c09ff
+size 498843504

tokenization_bart_japanese_news.py

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+# Copyright 2023 Stockmark Inc.
+#
+# 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.
+
+
+import os
+import unicodedata
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import regex
+import sentencepiece as spm
+from transformers import AddedToken, BartTokenizer, logging
+
+logger = logging.get_logger(__name__)
+VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "bart-base-japanese-news": "https://huggingface.co/stockmark/bart-base-japanese-news/resolve/main/spiece.model",
+    }}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "bart-base-japanese-news": 512,
+}
+
+SPIECE_UNDERLINE = "▁"
+
+
+class BartJapaneseNewsTokenizer(BartTokenizer):
+    """
+    Construct an Bart tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+
+    This tokenizer inherits from [`BartTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
+            contains the vocabulary necessary to instantiate a tokenizer.
+        do_lower_case (`bool`, *optional*, defaults to `False`):
+            Whether or not to lowercase the input when tokenizing.
+        remove_space (`bool`, *optional*, defaults to `False`):
+            Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).
+        clean_text (`bool`, *optional*, defaults to `False`):
+            Whether or not to clean input text
+
+        bos_token (`str`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+            <Tip>
+
+            When building a sequence using special tokens, this is not the token that is used for the beginning of
+            sequence. The token used is the `cls_token`.
+
+            </Tip>
+
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+
+            <Tip>
+
+            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+            The token used is the `sep_token`.
+
+            </Tip>
+
+        sep_token (`str`, *optional*, defaults to `"</s>"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        cls_token (`str`, *optional*, defaults to `"<s>"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (`str`, *optional*, defaults to `"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        mask_token (`str`, *optional*, defaults to `"<mask>"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+
+        sp_model_kwargs (`dict`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+
+    Attributes:
+        sp_model (`SentencePieceProcessor`):
+            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(
+        self,
+        vocab_file,
+        do_lower_case=False,
+        remove_space=False,
+        clean_text=False,
+        bos_token="<s>",
+        eos_token="</s>",
+        unk_token="<unk>",
+        sep_token="</s>",
+        pad_token="<pad>",
+        cls_token="<s>",
+        mask_token="<mask>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs
+    ) -> None:
+        # Mask token behave like a normal word, i.e. include the space before it and
+        # is included in the raw text, there should be a match in a non-normalized sentence.
+        mask_token = (
+            AddedToken(mask_token, lstrip=True, rstrip=True, normalized=False)
+            if isinstance(mask_token, str)
+            else mask_token
+        )
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        super(BartTokenizer, self).__init__(
+            do_lower_case=do_lower_case,
+            remove_space=remove_space,
+            clean_text=clean_text,
+            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,
+            sp_model_kwargs=self.sp_model_kwargs,
+            **kwargs,
+        )
+
+        self.do_lower_case = do_lower_case
+        self.remove_space = remove_space
+        self.clean_text = clean_text
+        self.vocab_file = vocab_file
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+
+    @property
+    def vocab_size(self):
+        return len(self.sp_model)
+
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
+
+    def preprocess_text(self, inputs):
+        if self.remove_space:
+            outputs = " ".join(inputs.strip().split())
+        else:
+            outputs = inputs
+
+        outputs = unicodedata.normalize("NFKD", outputs)
+        outputs = ''.join([ s for s in outputs if not self._is_control(s) ])
+
+        if self.clean_text:
+            outputs = outputs.replace('‘','\'').replace('’','\'').replace('“','"').replace('”','"')
+            outputs = outputs.replace('〈','<').replace('〉','>').replace('《','<').replace('》','>').replace('〔','【').replace('〕','】').replace('『','「').replace('』','」')
+            outputs = regex.sub(r'[\p{GeometricShapes}\p{MiscellaneousSymbols}]','*', outputs)
+            outputs = ''.join(regex.findall(r'[\p{InHiragana}\p{InKatakana}\p{BasicLatin}\p{Han}、。〃〆「」【】〒〜]',outputs))
+
+        if self.do_lower_case:
+            outputs = outputs.lower()
+
+        outputs = unicodedata.normalize('NFKC',outputs)
+        outputs = outputs.strip()
+        return outputs
+
+    def _tokenize(self, text: str) -> List[str]:
+        """Tokenize a string."""
+        text = self.preprocess_text(text)
+        pieces = self.sp_model.encode(text, out_type=str)
+        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 _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.PieceToId(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.sp_model.IdToPiece(index)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        return out_string.strip()
+
+    def _is_control(self, char):
+        '''
+        Check control char
+        Args:
+            char (str):
+        Returns:
+            bool:
+        '''
+        if char == "\t" or char == "\n" or char == "\r":
+            return False
+        cat = unicodedata.category(char)
+        if cat.startswith("C") or ord(char)==0 or ord(char)==0xfffd:
+            return True
+        return False
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+        return super(BartTokenizer, self).prepare_for_tokenization(text, is_split_into_words=False, **kwargs)

tokenizer_config.json

@@ -0,0 +1,27 @@
+{
+  "bos_token": "<s>",
+  "clean_text": false,
+  "cls_token": "<s>",
+  "do_lower_case": false,
+  "eos_token": "</s>",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "remove_space": false,
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "BartJapaneseNewsTokenizer",
+  "unk_token": "<unk>",
+  "auto_map": {
+     "AutoTokenizer": [
+        "tokenization_bart_japanese_news.BartJapaneseNewsTokenizer",
+        null
+     ]
+  }
+}