Source code for capreolus.reranker.TFVanillaBert

import tensorflow as tf
from transformers import TFBertForSequenceClassification

from capreolus import ConfigOption, Dependency
from capreolus.reranker import Reranker
from capreolus.utils.loginit import get_logger

[docs]logger = get_logger(__name__)
[docs]class TFVanillaBert_Class(tf.keras.layers.Layer): def __init__(self, extractor, config, *args, **kwargs): super(TFVanillaBert_Class, self).__init__(*args, **kwargs) self.extractor = extractor # TFBertForSequenceClassification contains both the BERT and the linear classifier layers self.bert = TFBertForSequenceClassification.from_pretrained(config["pretrained"], hidden_dropout_prob=0.1) assert extractor.config["numpassages"] == 1, "numpassages should be 1 for TFVanillaBERT" 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] doc_scores = self.bert(doc_bert_input, attention_mask=doc_mask, token_type_ids=doc_seg)[0] return doc_scores
[docs] def predict_step(self, data): 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 = tf.shape(posdoc_bert_input)[1] tf.debugging.assert_equal(num_passages, 1) maxseqlen = self.extractor.config["maxseqlen"] 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]) doc_scores =, posdoc_mask, posdoc_seg), training=False)[:, 1] tf.debugging.assert_equal(tf.shape(doc_scores), [batch_size]) return doc_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 TFVanillaBERT(Reranker): """ TensorFlow implementation of Vanilla BERT. Input is of the form [CLS] sentence A [SEP] sentence B [SEP] The "score" of a query (sentence A) - document (sentence B) pair is the probability that the document is relevant to the query. This is achieved through a linear classifier layer attached to BERT's last layer and using the logits[1] as the score. """
[docs] module_name = "TFVanillaBERT"
[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 to load")]
[docs] def build_model(self): self.model = TFVanillaBert_Class(self.extractor, self.config) return self.model