class

BERTForQuestionAnswering

extendsPretrainedModel

BERTForQuestionAnswering(config: BERTConfig)

source edit

BERT with a 2-way span head for extractive question answering.

Wraps the bidirectional encoder with a single linear of output width 2, producing start- and end-position logits over each token in the input. This is the SQuAD v1.1 / v2.0 fine-tuning recipe of Devlin et al., 2018 §4.2 — given a (question, context) pair concatenated with [SEP], the model predicts the answer span inside the context.

Parameters

configBERTConfig

BERT hyperparameters. The QA head is always 2-way; num_labels is ignored here.

Attributes

bertBERTModel

Underlying bidirectional encoder trunk.

qa_outputsnn.Linear

Final linear of shape (hidden_size, 2) mapping each token's hidden state to (start_logit, end_logit).

Notes

Reference: Devlin, Chang, Lee, and Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", NAACL 2019 (arXiv:1810.04805).

When both start_positions and end_positions are provided, the loss is the symmetric average of two cross-entropies:

\mathcal{L} = \tfrac{1}{2}\!\left( \mathrm{CE}(z^{\mathrm{start}}, y^{\mathrm{start}}) + \mathrm{CE}(z^{\mathrm{end}}, y^{\mathrm{end}}) \right).

The returned logits tensor has shape (B, T, 2); index [..., 0] for start scores and [..., 1] for end scores.

Examples

>>> import lucid
>>> from lucid.models.text.bert import BERTConfig, BERTForQuestionAnswering
>>> cfg = BERTConfig(num_hidden_layers=2, hidden_size=128,
...                  num_attention_heads=2, intermediate_size=512)
>>> model = BERTForQuestionAnswering(cfg).eval()
>>> input_ids = lucid.tensor([[101, 2040, 2003, 102, 1045, 2572, 102]])
>>> out = model(input_ids)
>>> out.logits.shape   # (B=1, T=7, 2)
(1, 7, 2)

Used by 2

Constructors

dunder

init

→None

__init__(config: BERTConfig)

source edit

Instance methods

forward

→MaskedLMOutput

forward(input_ids: Tensor, attention_mask: Tensor | None = None, token_type_ids: Tensor | None = None, start_positions: Tensor | None = None, end_positions: Tensor | None = None)

source edit

>>> import lucid >>> from lucid.models.text.bert import BERTConfig, BERTForQuestionAnswering >>> cfg = BERTConfig(num_hidden_layers=2, hidden_size=128, ... num_attention_heads=2, intermediate_size=512) >>> model = BERTForQuestionAnswering(cfg).eval() >>> input_ids = lucid.tensor([[101, 2040, 2003, 102, 1045, 2572, 102]]) >>> out = model(input_ids) >>> out.logits.shape # (B=1, T=7, 2) (1, 7, 2)