Source code for capreolus.reranker.TFBERTMaxP

import tensorflow as tf
from transformers import TFAutoModelForSequenceClassification

from capreolus import ConfigOption, Dependency
from capreolus.reranker import Reranker

[docs]class TFElectraRelevanceHead(tf.keras.layers.Layer): """ BERT-style ClassificationHead (i.e., out_proj only -- no dense). See transformers.TFElectraClassificationHead """ def __init__(self, dropout, out_proj, *args, **kwargs): super().__init__(*args, **kwargs) self.dropout = dropout self.out_proj = out_proj
[docs] def call(self, inputs, **kwargs): x = inputs[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.out_proj(x) return x
[docs]class TFBERTMaxP_Class(tf.keras.layers.Layer): def __init__(self, extractor, config, *args, **kwargs): super(TFBERTMaxP_Class, self).__init__(*args, **kwargs) self.extractor = extractor if config["pretrained"] == "electra-base-msmarco": self.bert = TFAutoModelForSequenceClassification.from_pretrained("Capreolus/electra-base-msmarco") dropout, fc = self.bert.classifier.dropout, self.bert.classifier.out_proj self.bert.classifier = TFElectraRelevanceHead(dropout, fc) elif config["pretrained"] == "bert-base-msmarco": self.bert = TFAutoModelForSequenceClassification.from_pretrained("Capreolus/bert-base-msmarco") else: self.bert = TFAutoModelForSequenceClassification.from_pretrained( config["pretrained"], hidden_dropout_prob=config["hidden_dropout_prob"] ) self.config = config
[docs] def call(self, x, **kwargs): """ Returns logits of shape [2] """ doc_bert_input, doc_mask, doc_seg = x[0], x[1], x[2] if "roberta" in self.config["pretrained"]: doc_seg = tf.zeros_like(doc_mask) # since roberta does not have segment input passage_scores = self.bert(doc_bert_input, attention_mask=doc_mask, token_type_ids=doc_seg)[0] return passage_scores
[docs] def predict_step(self, data): """ Scores each passage and applies max pooling over it. """ posdoc_bert_input, posdoc_mask, posdoc_seg, negdoc_bert_input, negdoc_mask, negdoc_seg = data batch_size = tf.shape(posdoc_bert_input)[0] num_passages = self.extractor.config["numpassages"] maxseqlen = self.extractor.config["maxseqlen"] passage_position = tf.reduce_sum(posdoc_mask * posdoc_seg, axis=-1) # (B, P) passage_mask = tf.cast(tf.greater(passage_position, 5), tf.float32) # (B, P) posdoc_bert_input = tf.reshape(posdoc_bert_input, [batch_size * num_passages, maxseqlen]) posdoc_mask = tf.reshape(posdoc_mask, [batch_size * num_passages, maxseqlen]) posdoc_seg = tf.reshape(posdoc_seg, [batch_size * num_passages, maxseqlen]) passage_scores =, posdoc_mask, posdoc_seg), training=False)[:, 1] passage_scores = tf.reshape(passage_scores, [batch_size, num_passages]) if self.config["aggregation"] == "max": passage_scores = tf.math.reduce_max(passage_scores, axis=1) elif self.config["aggregation"] == "first": passage_scores = passage_scores[:, 0] elif self.config["aggregation"] == "sum": passage_scores = tf.math.reduce_sum(passage_mask * passage_scores, axis=1) elif self.config["aggregation"] == "avg": passage_scores = tf.math.reduce_sum(passage_mask * passage_scores, axis=1) / tf.reduce_sum(passage_mask) else: raise ValueError("Unknown aggregation method: {}".format(self.config["aggregation"])) return passage_scores
[docs] def score(self, x, **kwargs): posdoc_bert_input, posdoc_mask, posdoc_seg, negdoc_bert_input, negdoc_mask, negdoc_seg = x return, posdoc_mask, posdoc_seg), **kwargs)
[docs] def score_pair(self, x, **kwargs): posdoc_bert_input, posdoc_mask, posdoc_seg, negdoc_bert_input, negdoc_mask, negdoc_seg = x pos_score =, posdoc_mask, posdoc_seg), **kwargs)[:, 1] neg_score =, negdoc_mask, negdoc_seg), **kwargs)[:, 1] return pos_score, neg_score
[docs]@Reranker.register class TFBERTMaxP(Reranker): """ TensorFlow implementation of BERT-MaxP. Deeper Text Understanding for IR with Contextual Neural Language Modeling. Zhuyun Dai and Jamie Callan. SIGIR 2019. """
[docs] module_name = "TFBERTMaxP"
[docs] dependencies = [ Dependency(key="extractor", module="extractor", name="bertpassage"), Dependency(key="trainer", module="trainer", name="tensorflow"),
[docs] config_spec = [ ConfigOption( "pretrained", "bert-base-uncased", "Pretrained model: bert-base-uncased, bert-base-msmarco, electra-base-msmarco, or HuggingFace supported models", ), ConfigOption("aggregation", "max"), ConfigOption("hidden_dropout_prob", 0.1, "The dropout probability of BERT-like model's hidden layers."),
[docs] def build_model(self): self.model = TFBERTMaxP_Class(self.extractor, self.config) return self.model