add weight

README.md

@@ -0,0 +1 @@
+# RoBERTa for Vietnamese and English (envibert)

config.json

@@ -0,0 +1,27 @@
+{
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.10.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 59993
+}

envibert_tokenizer.py

@@ -0,0 +1,317 @@
+# !pip install sentencepiece==0.1.96 transformers==4.10.0
+import sentencepiece as spm
+import os
+from transformers import PreTrainedTokenizer
+from collections import Counter
+from typing import List, Optional
+
+
+class RobertaTokenizer(PreTrainedTokenizer):
+    def __init__(
+            self,
+            pretrained_file,
+            bos_token="<s>",
+            eos_token="</s>",
+            sep_token="</s>",
+            cls_token="<s>",
+            unk_token="<unk>",
+            pad_token="<pad>",
+            mask_token="<mask>",
+            **kwargs
+    ):
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            **kwargs,
+        )
+
+        # load bpe model and vocab file
+        sentencepiece_model = os.path.join(pretrained_file, 'sentencepiece.bpe.model')
+        vocab_file = os.path.join(pretrained_file, 'dict.txt')
+        self.sp_model = spm.SentencePieceProcessor()
+        self.sp_model.Load(
+            sentencepiece_model)  # please dont use anything from sp_model bcz it makes everything goes wrong
+
+        self.bpe_dict = Dictionary().load(vocab_file)
+
+        # Mimic fairseq token-to-id alignment for the first 4 token
+        self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
+
+        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
+        self.fairseq_offset = 0
+
+        self.fairseq_tokens_to_ids["<mask>"] = len(self.bpe_dict) + self.fairseq_offset
+        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
+
+    def _tokenize(self, text):
+        return self.sp_model.EncodeAsPieces(text)
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        if token in self.fairseq_tokens_to_ids:
+            return self.fairseq_tokens_to_ids[token]
+        spm_id = self.bpe_dict.index(token)
+        return spm_id
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        if index in self.fairseq_ids_to_tokens:
+            return self.fairseq_ids_to_tokens[index]
+        return self.bpe_dict[index]
+
+    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.
+
+        This implementation does not add special tokens and this method should be overridden in a subclass.
+
+        Args:
+            token_ids_0 (:obj:`List[int]`): The first tokenized sequence.
+            token_ids_1 (:obj:`List[int]`, `optional`): The second tokenized sequence.
+
+        Returns:
+            :obj:`List[int]`: The model input with special tokens.
+        """
+        return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+    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. XLM-RoBERTa does
+        not make use of token type ids, therefore a list of zeros is returned.
+
+        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 zeros.
+
+        """
+
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        return len(cls + token_ids_0 + sep) * [0]
+
+    @property
+    def vocab_size(self):
+        return len(self.bpe_dict) + self.fairseq_offset + 1  # Add the <mask> token
+
+    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
+
+
+class Dictionary(object):
+    """A mapping from symbols to consecutive integers"""
+
+    def __init__(
+            self,
+            pad='<pad>',
+            eos='</s>',
+            unk='<unk>',
+            bos='<s>',
+            extra_special_symbols=None,
+    ):
+        self.unk_word, self.pad_word, self.eos_word = unk, pad, eos
+        self.symbols = []
+        self.count = []
+        self.indices = {}
+        self.bos_index = self.add_symbol(bos)
+        self.pad_index = self.add_symbol(pad)
+        self.eos_index = self.add_symbol(eos)
+        self.unk_index = self.add_symbol(unk)
+        if extra_special_symbols:
+            for s in extra_special_symbols:
+                self.add_symbol(s)
+        self.nspecial = len(self.symbols)
+
+    def __eq__(self, other):
+        return self.indices == other.indices
+
+    def __getitem__(self, idx):
+        if idx < len(self.symbols):
+            return self.symbols[idx]
+        return self.unk_word
+
+    def __len__(self):
+        """Returns the number of symbols in the dictionary"""
+        return len(self.symbols)
+
+    def __contains__(self, sym):
+        return sym in self.indices
+
+    def index(self, sym):
+        """Returns the index of the specified symbol"""
+        assert isinstance(sym, str)
+        if sym in self.indices:
+            return self.indices[sym]
+        return self.unk_index
+
+    def unk_string(self, escape=False):
+        """Return unknown string, optionally escaped as: <<unk>>"""
+        if escape:
+            return '<{}>'.format(self.unk_word)
+        else:
+            return self.unk_word
+
+    def add_symbol(self, word, n=1):
+        """Adds a word to the dictionary"""
+        if word in self.indices:
+            idx = self.indices[word]
+            self.count[idx] = self.count[idx] + n
+            return idx
+        else:
+            idx = len(self.symbols)
+            self.indices[word] = idx
+            self.symbols.append(word)
+            self.count.append(n)
+            return idx
+
+    def update(self, new_dict):
+        """Updates counts from new dictionary."""
+        for word in new_dict.symbols:
+            idx2 = new_dict.indices[word]
+            if word in self.indices:
+                idx = self.indices[word]
+                self.count[idx] = self.count[idx] + new_dict.count[idx2]
+            else:
+                idx = len(self.symbols)
+                self.indices[word] = idx
+                self.symbols.append(word)
+                self.count.append(new_dict.count[idx2])
+
+    def finalize(self, threshold=-1, nwords=-1, padding_factor=8):
+        """Sort symbols by frequency in descending order, ignoring special ones.
+
+        Args:
+            - threshold defines the minimum word count
+            - nwords defines the total number of words in the final dictionary,
+                including special symbols
+            - padding_factor can be used to pad the dictionary size to be a
+                multiple of 8, which is important on some hardware (e.g., Nvidia
+                Tensor Cores).
+        """
+        if nwords <= 0:
+            nwords = len(self)
+
+        new_indices = dict(zip(self.symbols[:self.nspecial], range(self.nspecial)))
+        new_symbols = self.symbols[:self.nspecial]
+        new_count = self.count[:self.nspecial]
+
+        c = Counter(dict(sorted(zip(self.symbols[self.nspecial:], self.count[self.nspecial:]))))
+        for symbol, count in c.most_common(nwords - self.nspecial):
+            if count >= threshold:
+                new_indices[symbol] = len(new_symbols)
+                new_symbols.append(symbol)
+                new_count.append(count)
+            else:
+                break
+
+        threshold_nwords = len(new_symbols)
+        if padding_factor > 1:
+            i = 0
+            while threshold_nwords % padding_factor != 0:
+                symbol = 'madeupword{:04d}'.format(i)
+                new_indices[symbol] = len(new_symbols)
+                new_symbols.append(symbol)
+                new_count.append(0)
+                i += 1
+                threshold_nwords += 1
+
+        assert len(new_symbols) % padding_factor == 0
+        assert len(new_symbols) == len(new_indices)
+
+        self.count = list(new_count)
+        self.symbols = list(new_symbols)
+        self.indices = new_indices
+
+    def bos(self):
+        """Helper to get index of beginning-of-sentence symbol"""
+        return self.bos_index
+
+    def pad(self):
+        """Helper to get index of pad symbol"""
+        return self.pad_index
+
+    def eos(self):
+        """Helper to get index of end-of-sentence symbol"""
+        return self.eos_index
+
+    def unk(self):
+        """Helper to get index of unk symbol"""
+        return self.unk_index
+
+    @classmethod
+    def load(cls, f):
+        """Loads the dictionary from a text file with the format:
+
+        ```
+        <symbol0> <count0>
+        <symbol1> <count1>
+        ...
+        ```
+        """
+        d = cls()
+        d.add_from_file(f)
+        return d
+
+    def add_from_file(self, f):
+        """
+        Loads a pre-existing dictionary from a text file and adds its symbols
+        to this instance.
+        """
+        if isinstance(f, str):
+            try:
+                with open(f, 'r', encoding='utf-8') as fd:
+                    self.add_from_file(fd)
+            except FileNotFoundError as fnfe:
+                raise fnfe
+            except UnicodeError:
+                raise Exception("Incorrect encoding detected in {}, please "
+                                "rebuild the dataset".format(f))
+            return
+
+        lines = f.readlines()
+        indices_start_line = self._load_meta(lines)
+        for line in lines[indices_start_line:]:
+            idx = line.rfind(' ')
+            if idx == -1:
+                raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
+            word = line[:idx]
+            count = int(line[idx + 1:])
+            self.indices[word] = len(self.symbols)
+            self.symbols.append(word)
+            self.count.append(count)
+
+    def _save(self, f, kv_iterator):
+        if isinstance(f, str):
+            os.makedirs(os.path.dirname(f), exist_ok=True)
+            with open(f, 'w', encoding='utf-8') as fd:
+                return self.save(fd)
+        for k, v in kv_iterator:
+            print('{} {}'.format(k, v), file=f)
+
+    def _get_meta(self):
+        return [], []
+
+    def _load_meta(self, lines):
+        return 0
+
+    def save(self, f):
+        """Stores dictionary into a text file"""
+        ex_keys, ex_vals = self._get_meta()
+        self._save(f, zip(ex_keys + self.symbols[self.nspecial:], ex_vals + self.count[self.nspecial:]))

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f497425b7753cda40fbd04627c79c7407577a0751934271c10f61092fd32f37
+size 283095275

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:129e177c6321693d7e1179d0297fb030d914c108f3249edb69d21048dae7222a
+size 1269105