RetriBERT¶

Overview¶

The RetriBERT model was proposed in the blog post Explain Anything Like I’m Five: A Model for Open Domain Long Form Question Answering, RetriBERT is a small model that uses either a single or pair of Bert encoders with lower-dimension projection for dense semantic indexing of text.

Code to train and use the model can be found here.

RetriBertConfig¶

class transformers.RetriBertConfig(vocab_size=30522, hidden_size=768, num_hidden_layers=8, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, share_encoders=True, projection_dim=128, pad_token_id=0, **kwargs)[source]¶

This is the configuration class to store the configuration of a RetriBertModel. It is used to instantiate a RetriBertModel model according to the specified arguments, defining the model architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Parameters

  • vocab_size (int, optional, defaults to 30522) – Vocabulary size of the BERT model. Defines the different tokens that can be represented by the inputs_ids passed to the forward method of BertModel.

  • hidden_size (int, optional, defaults to 768) – Dimensionality of the encoder layers and the pooler layer.

  • num_hidden_layers (int, optional, defaults to 12) – Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional, defaults to 12) – Number of attention heads for each attention layer in the Transformer encoder.

  • intermediate_size (int, optional, defaults to 3072) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

  • hidden_act (str or function, optional, defaults to “gelu”) – The non-linear activation function (function or string) in the encoder and pooler. If string, “gelu”, “relu”, “swish” and “gelu_new” are supported.

  • hidden_dropout_prob (float, optional, defaults to 0.1) – The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_probs_dropout_prob (float, optional, defaults to 0.1) – The dropout ratio for the attention probabilities.

  • max_position_embeddings (int, optional, defaults to 512) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048).

  • type_vocab_size (int, optional, defaults to 2) – The vocabulary size of the token_type_ids passed into BertModel.

  • initializer_range (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • layer_norm_eps (float, optional, defaults to 1e-12) – The epsilon used by the layer normalization layers.

  • share_encoders (bool, optional, defaults to True) – Whether to use the same Bert-type encoder for the queries and document

  • projection_dim (int, optional, defaults to 128) – Final dimension of the query and document representation after projection

RetriBertTokenizer¶

class transformers.RetriBertTokenizer(vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, **kwargs)[source]¶

Constructs a retribert.

BertTokenizer and runs end-to-end tokenization: punctuation splitting + wordpiece.

Refer to superclass BertTokenizer for usage examples and documentation concerning parameters.

RetriBertTokenizerFast¶

class transformers.RetriBertTokenizerFast(vocab_file, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', clean_text=True, tokenize_chinese_chars=True, strip_accents=None, wordpieces_prefix='##', **kwargs)[source]¶

Constructs a “Fast” RetriBertTokenizerFast (backed by HuggingFace’s tokenizers library).

RetriBertTokenizerFast is identical to BertTokenizerFast and runs end-to-end tokenization: punctuation splitting + wordpiece.

Refer to superclass BertTokenizerFast for usage examples and documentation concerning parameters.

RetriBertModel¶

class transformers.RetriBertModel(config)[source]¶

Bert Based model to embed queries or document for document retreival.

This model is a PyTorch torch.nn.Module sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (RetriBertConfig) – Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

forward(input_ids_query, attention_mask_query, input_ids_doc, attention_mask_doc, checkpoint_batch_size=- 1)[source]¶
Parameters

  • input_ids_query (torch.LongTensor of shape (batch_size, sequence_length)) –

    Indices of input sequence tokens in the vocabulary for the queries in a batch.

    Indices can be obtained using transformers.RetriBertTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are input IDs?

  • attention_mask_query (torch.FloatTensor of shape (batch_size, sequence_length), optional, defaults to None) –

    Mask to avoid performing attention on queries padding token indices. Mask values selected in [0, 1]: 1 for tokens that are NOT MASKED, 0 for MASKED tokens.

    What are attention masks?

  • input_ids_doc (torch.LongTensor of shape (batch_size, sequence_length)) – Indices of input sequence tokens in the vocabulary for the documents in a batch.

  • attention_mask_doc (torch.FloatTensor of shape (batch_size, sequence_length), optional, defaults to None) – Mask to avoid performing attention on documents padding token indices.

  • checkpoint_batch_size (int, optional, defaults to :obj:-1`) – If greater than 0, uses gradient checkpointing to only compute sequence representation on checkpoint_batch_size examples at a time on the GPU. All query representations are still compared to all document representations in the batch.

Returns

torch.FloatTensor the bi-directional cross-entropy loss obtained while trying to match each query to its corresponding document and each cocument to its corresponding query in the batch