[RoBERTa] model conversion, inference, tests 🔥

README.md

@@ -12,6 +12,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
 4. **[Transformer-XL](https://github.com/kimiyoung/transformer-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 5. **[XLNet](https://github.com/zihangdai/xlnet/)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
 6. **[XLM](https://github.com/facebookresearch/XLM/)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
+7. **[RoBERTa](https://github.com/pytorch/fairseq/tree/master/examples/roberta)** (from Facebook), a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott et al.
 
 These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/pytorch-transformers/examples.html).
 

pytorch_transformers/convert_roberta_checkpoint_to_pytorch.py

+# coding=utf-8
+# Copyright 2018 The HuggingFace Inc. team.
+#
+# 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.
+"""Convert RoBERTa checkpoint."""
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import logging
+import numpy as np
+import torch
+
+from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
+from fairseq.modules import TransformerSentenceEncoderLayer
+from pytorch_transformers.modeling_bert import (BertConfig, BertEncoder,
+                                                BertIntermediate, BertLayer,
+                                                BertModel, BertOutput,
+                                                BertSelfAttention,
+                                                BertSelfOutput)
+from pytorch_transformers.modeling_roberta import (RobertaEmbeddings,
+                                                   RobertaForMaskedLM,
+                                                   RobertaModel)
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+SAMPLE_TEXT = 'Hello world! cécé herlolip'
+
+
+def convert_roberta_checkpoint_to_pytorch(roberta_checkpoint_path, pytorch_dump_folder_path):
+    """
+    Copy/paste/tweak roberta's weights to our BERT structure.
+    """
+    roberta = FairseqRobertaModel.from_pretrained(roberta_checkpoint_path)
+    roberta.eval()  # disable dropout
+    config = BertConfig(
+        vocab_size_or_config_json_file=50265,
+        hidden_size=roberta.args.encoder_embed_dim,
+        num_hidden_layers=roberta.args.encoder_layers,
+        num_attention_heads=roberta.args.encoder_attention_heads,
+        intermediate_size=roberta.args.encoder_ffn_embed_dim,
+        max_position_embeddings=514,
+        type_vocab_size=1,
+    )
+    print("Our BERT config:", config)
+
+    model = RobertaForMaskedLM(config)
+    model.eval()
+
+    # Now let's copy all the weights.
+    # Embeddings
+    roberta_sent_encoder = roberta.model.decoder.sentence_encoder
+    model.roberta.embeddings.word_embeddings.weight = roberta_sent_encoder.embed_tokens.weight
+    model.roberta.embeddings.position_embeddings.weight = roberta_sent_encoder.embed_positions.weight
+    model.roberta.embeddings.token_type_embeddings.weight.data = torch.zeros_like(model.roberta.embeddings.token_type_embeddings.weight)  # just zero them out b/c RoBERTa doesn't use them.
+    model.roberta.embeddings.LayerNorm.weight = roberta_sent_encoder.emb_layer_norm.weight
+    model.roberta.embeddings.LayerNorm.bias = roberta_sent_encoder.emb_layer_norm.bias
+    model.roberta.embeddings.LayerNorm.variance_epsilon = roberta_sent_encoder.emb_layer_norm.eps
+
+    for i in range(config.num_hidden_layers):
+        # Encoder: start of layer
+        layer: BertLayer = model.roberta.encoder.layer[i]
+        roberta_layer: TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
+
+        ### self attention
+        self_attn: BertSelfAttention = layer.attention.self
+        assert(
+            roberta_layer.self_attn.in_proj_weight.shape == torch.Size((3 * config.hidden_size, config.hidden_size))
+        )
+        # we use three distinct linear layers so we split the source layer here.
+        self_attn.query.weight.data = roberta_layer.self_attn.in_proj_weight[:config.hidden_size, :]
+        self_attn.query.bias.data = roberta_layer.self_attn.in_proj_bias[:config.hidden_size]
+        self_attn.key.weight.data = roberta_layer.self_attn.in_proj_weight[config.hidden_size:2*config.hidden_size, :]
+        self_attn.key.bias.data = roberta_layer.self_attn.in_proj_bias[config.hidden_size:2*config.hidden_size]
+        self_attn.value.weight.data = roberta_layer.self_attn.in_proj_weight[2*config.hidden_size:, :]
+        self_attn.value.bias.data = roberta_layer.self_attn.in_proj_bias[2*config.hidden_size:]
+
+        ### self-attention output
+        self_output: BertSelfOutput = layer.attention.output
+        assert(
+            self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
+        )
+        self_output.dense.weight = roberta_layer.self_attn.out_proj.weight
+        self_output.dense.bias = roberta_layer.self_attn.out_proj.bias
+        self_output.LayerNorm.weight = roberta_layer.self_attn_layer_norm.weight
+        self_output.LayerNorm.bias = roberta_layer.self_attn_layer_norm.bias
+        self_output.LayerNorm.variance_epsilon = roberta_layer.self_attn_layer_norm.eps
+
+        ### intermediate
+        intermediate: BertIntermediate = layer.intermediate
+        assert(
+            intermediate.dense.weight.shape == roberta_layer.fc1.weight.shape
+        )
+        intermediate.dense.weight = roberta_layer.fc1.weight
+        intermediate.dense.bias = roberta_layer.fc1.bias
+
+        ### output
+        bert_output: BertOutput = layer.output
+        assert(
+            bert_output.dense.weight.shape == roberta_layer.fc2.weight.shape
+        )
+        bert_output.dense.weight = roberta_layer.fc2.weight
+        bert_output.dense.bias = roberta_layer.fc2.bias
+        bert_output.LayerNorm.weight = roberta_layer.final_layer_norm.weight
+        bert_output.LayerNorm.bias = roberta_layer.final_layer_norm.bias
+        bert_output.LayerNorm.variance_epsilon = roberta_layer.final_layer_norm.eps
+        #### end of layer
+    
+    # LM Head
+    model.lm_head.dense.weight = roberta.model.decoder.lm_head.dense.weight
+    model.lm_head.dense.bias = roberta.model.decoder.lm_head.dense.bias
+    model.lm_head.layer_norm.weight = roberta.model.decoder.lm_head.layer_norm.weight
+    model.lm_head.layer_norm.bias = roberta.model.decoder.lm_head.layer_norm.bias
+    model.lm_head.layer_norm.variance_epsilon = roberta.model.decoder.lm_head.layer_norm.eps
+    model.lm_head.weight = roberta.model.decoder.lm_head.weight
+    model.lm_head.bias = roberta.model.decoder.lm_head.bias
+
+    # Let's check that we get the same results.
+    input_ids: torch.Tensor = roberta.encode(SAMPLE_TEXT).unsqueeze(0) # batch of size 1
+
+    our_output = model(input_ids)[0]
+    their_output = roberta.model(input_ids)[0]
+    print(our_output.shape, their_output.shape)
+    success = torch.allclose(our_output, their_output, atol=1e-3)
+    print(
+        "Do both models output the same tensors?",
+        "🔥" if success else "💩"
+    )
+    if not success:
+        raise Exception("Something went wRoNg")
+
+    print(f"Saving model to {pytorch_dump_folder_path}")
+    model.save_pretrained(pytorch_dump_folder_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    ## Required parameters
+    parser.add_argument("--roberta_checkpoint_path",
+                        default = None,
+                        type = str,
+                        required = True,
+                        help = "Path the official PyTorch dump.")
+    parser.add_argument("--pytorch_dump_folder_path",
+                        default = None,
+                        type = str,
+                        required = True,
+                        help = "Path to the output PyTorch model.")
+    args = parser.parse_args()
+    convert_roberta_checkpoint_to_pytorch(
+        args.roberta_checkpoint_path,
+        args.pytorch_dump_folder_path
+    )

pytorch_transformers/modeling_roberta.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+"""PyTorch RoBERTa model. """
+
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from pytorch_transformers.modeling_bert import (BertConfig, BertEmbeddings,
+                                                BertLayerNorm, BertModel,
+                                                BertPreTrainedModel, gelu)
+
+logger = logging.getLogger(__name__)
+
+ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
+    'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-pytorch_model.bin",
+    'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-pytorch_model.bin",
+    'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-pytorch_model.bin",
+}
+
+ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json",
+    'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json",
+    'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-config.json",
+}
+
+
+class RobertaEmbeddings(BertEmbeddings):
+    """
+    Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
+    """
+    def __init__(self, config):
+        super(RobertaEmbeddings, self).__init__(config)
+        self.padding_idx = 1
+
+    def forward(self, input_ids, token_type_ids=None, position_ids=None):
+        seq_length = input_ids.size(1)
+        if position_ids is None:
+            # Position numbers begin at padding_idx+1. Padding symbols are ignored.
+            # cf. fairseq's `utils.make_positions`
+            position_ids = torch.arange(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=torch.long, device=input_ids.device)
+            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        return super().forward(input_ids, token_type_ids=token_type_ids, position_ids=position_ids)
+
+
+class RobertaConfig(BertConfig):
+    pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
+
+class RobertaModel(BertModel):
+    """
+    Same as BertModel with:
+    - a tiny embeddings tweak.
+    - setup for Roberta pretrained models
+    """
+    config_class = RobertaConfig
+    pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+    base_model_prefix = "roberta"
+
+    def __init__(self, config):
+        super(RobertaModel, self).__init__(config)
+
+        self.embeddings = RobertaEmbeddings(config)
+
+
+
+class RobertaForMaskedLM(BertPreTrainedModel):
+    """
+    Roberta Model with a `language modeling` head on top.
+    """
+    config_class = RobertaConfig
+    pretrained_model_archive_map = ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
+    base_model_prefix = "roberta"
+
+    def __init__(self, config):
+        super(RobertaForMaskedLM, self).__init__(config)
+
+        self.roberta = RobertaModel(config)
+        self.lm_head = RobertaLMHead(config)
+    
+    def forward(self, input_ids, token_type_ids=None, attention_mask=None, position_ids=None, head_mask=None):
+        outputs = self.roberta(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
+                            attention_mask=attention_mask, head_mask=head_mask)
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        outputs = (prediction_scores,) + outputs[2:]
+        return outputs
+
+
+
+class RobertaLMHead(nn.Module):
+    """Roberta Head for masked language modeling."""
+
+    def __init__(self, config: BertConfig):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.layer_norm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.weight = nn.Linear(config.hidden_size, config.vocab_size, bias=False).weight
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+    def forward(self, features, **kwargs):
+        x = self.dense(features)
+        x = gelu(x)
+        x = self.layer_norm(x)
+
+        # project back to size of vocabulary with bias
+        x = F.linear(x, self.weight) + self.bias
+
+        return x

pytorch_transformers/tests/modeling_roberta_test.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team 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 __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import os
+import unittest
+import pytest
+import torch
+
+from pytorch_transformers.modeling_roberta import (RobertaForMaskedLM,
+                                                   RobertaModel)
+
+
+class RobertaModelTest(unittest.TestCase):
+
+    # @pytest.mark.slow
+    def test_inference_masked_lm(self):
+        model = RobertaForMaskedLM.from_pretrained('roberta-base')
+        
+        input_ids = torch.tensor([[    0, 31414,   232,   328,   740,  1140, 12695,    69, 46078,  1588,   2]])
+        output = model(input_ids)[0]
+        expected_shape = torch.Size((1, 11, 50265))
+        self.assertEqual(
+            output.shape,
+            expected_shape
+        )
+        # compare the actual values for a slice.
+        expected_slice = torch.Tensor(
+            [[[33.8843, -4.3107, 22.7779],
+              [ 4.6533, -2.8099, 13.6252],
+              [ 1.8222, -3.6898,  8.8600]]]
+        )
+        self.assertTrue(
+            torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
+        )
+
+    # @pytest.mark.slow
+    def test_inference_no_head(self):
+        model = RobertaModel.from_pretrained('roberta-base')
+        
+        input_ids = torch.tensor([[    0, 31414,   232,   328,   740,  1140, 12695,    69, 46078,  1588,   2]])
+        output = model(input_ids)[0]
+        # compare the actual values for a slice.
+        expected_slice = torch.Tensor(
+            [[[-0.0231,  0.0782,  0.0074],
+              [-0.1854,  0.0539, -0.0174],
+              [ 0.0548,  0.0799,  0.1687]]]
+        )
+        self.assertTrue(
+            torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
+        )
+
+
+
+if __name__ == '__main__':
+    unittest.main()

pytorch_transformers/tests/tokenization_roberta_test.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team 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 __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import os
+import unittest
+import pytest
+
+from pytorch_transformers.tokenization_roberta import RobertaTokenizer
+
+
+class RobertaTokenizationTest(unittest.TestCase):
+
+    # @pytest.mark.slow
+    def test_full_tokenizer(self):
+        tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
+        self.assertListEqual(
+            tokenizer.encode('Hello world!'),
+            [0, 31414, 232, 328, 2]
+        )
+        self.assertListEqual(
+            tokenizer.encode('Hello world! cécé herlolip'),
+            [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]
+        )
+
+
+
+if __name__ == '__main__':
+    unittest.main()

pytorch_transformers/tokenization_roberta.py

+# coding=utf-8
+# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
+#
+# 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.
+"""Tokenization classes for RoBERTa."""
+from __future__ import (absolute_import, division, print_function,
+                        unicode_literals)
+
+import json
+import logging
+import re
+
+from .tokenization_utils import PreTrainedTokenizer
+from .tokenization_gpt2 import GPT2Tokenizer
+
+logger = logging.getLogger(__name__)
+
+VOCAB_FILES_NAMES = {
+    'dict_file': 'dict.txt',
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    'dict_file':
+    {
+        'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
+        'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
+        'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-dict.txt",
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    'roberta-base': 512,
+    'roberta-large': 512,
+    'roberta-large-mnli': 512,
+}
+
+
+SPACE_NORMALIZER = re.compile(r"\s+")
+
+def tokenize_line(line):
+    line = SPACE_NORMALIZER.sub(" ", line)
+    line = line.strip()
+    return line.split()
+
+
+class Dictionary(object):
+    """
+    A mapping from symbols to consecutive integers
+
+    From Facebook's fairseq.
+    """
+
+    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 __getitem__(self, idx):
+        if idx < len(self.symbols):
+            return self.symbols[idx]
+        return self.unk_word
+
+    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 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
+
+    @classmethod
+    def load(cls, f, ignore_utf_errors=False):
+        """Loads the dictionary from a text file with the format:
+
+        ```
+        <symbol0> <count0>
+        <symbol1> <count1>
+        ...
+        ```
+        """
+        d = cls()
+        d.add_from_file(f, ignore_utf_errors)
+        return d
+
+    def add_from_file(self, f, ignore_utf_errors=False):
+        """
+        Loads a pre-existing dictionary from a text file and adds its symbols
+        to this instance.
+        """
+        if isinstance(f, str):
+            try:
+                if not ignore_utf_errors:
+                    with open(f, 'r', encoding='utf-8') as fd:
+                        self.add_from_file(fd)
+                else:
+                    with open(f, 'r', encoding='utf-8', errors='ignore') 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()
+        for line in lines:
+            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 encode_line(self, line, line_tokenizer=tokenize_line, add_if_not_exist=True,
+                    consumer=None, append_eos=True, reverse_order=False):
+        words = line_tokenizer(line)
+        if reverse_order:
+            words = list(reversed(words))
+        nwords = len(words)
+        ids = [0] * (nwords + 1 if append_eos else nwords)
+
+        for i, word in enumerate(words):
+            if add_if_not_exist:
+                idx = self.add_symbol(word)
+            else:
+                idx = self.index(word)
+            if consumer is not None:
+                consumer(word, idx)
+            ids[i] = idx
+        if append_eos:
+            ids[nwords] = self.eos_index
+        return ids
+
+
+
+
+class RobertaTokenizer(PreTrainedTokenizer):
+    """
+    RoBERTa tokenizer. Peculiarities:
+        - GPT-2 tokenizer with a different integer mapping on top.
+    """
+    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, dict_file,
+                 bos_token="<s>", eos_token="</s>", **kwargs):
+        super(RobertaTokenizer, self).__init__(bos_token=bos_token, eos_token=eos_token, **kwargs)
+
+        self.gpt2_tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+        self.dictionary = Dictionary.load(dict_file)
+
+    def _tokenize(self, text):
+        """ Use GPT-2 Tokenizer """
+        return self.gpt2_tokenizer._tokenize(text)
+
+    def encode(self, text):
+        """ Converts a string in a sequence of ids (integer), using the tokenizer and vocabulary.
+        """
+        gpt2_tokens_joined = " ".join(
+            str(x) for x in self.gpt2_tokenizer.convert_tokens_to_ids(self.tokenize(text))
+        )
+        bpe_sentence = '<s> ' + gpt2_tokens_joined + ' </s>'
+        return self.dictionary.encode_line(bpe_sentence, append_eos=False)
+
+    def _convert_token_to_id(self, token):
+        return self.dictionary.index(token)
+
+    def _convert_id_to_token(self, index):
+        symbol = self.dictionary[index]
+        try:
+            idx = int(symbol)
+            return self.gpt2_tokenizer._convert_id_to_token(idx)
+        except:
+            return symbol
+
+    def convert_tokens_to_string(self, tokens):
+        return self.gpt2_tokenizer.convert_tokens_to_string(tokens)