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Types & classes28 in github.com/arijitx/QnA-Bot

↓ 7 callersClassBertModel
BERT model ("Bidirectional Embedding Representations from a Transformer"). Params: config: a BertConfig class instance with the configura
modeling.py:668
↓ 4 callersClassBertLayerNorm
modeling.py:247
↓ 2 callersClassBertLMPredictionHead
modeling.py:462
↓ 2 callersClassCoQAExample
A single training/test example for the CoQA dataset. For examples without an answer, the start and end position are -1.
infer_utils.py:31
↓ 1 callersClassBertAttention
modeling.py:358
↓ 1 callersClassBertConfig
Configuration class to store the configuration of a `BertModel`.
modeling.py:150
↓ 1 callersClassBertEmbeddings
Construct the embeddings from word, position and token_type embeddings.
modeling.py:262
↓ 1 callersClassBertEncoder
modeling.py:413
↓ 1 callersClassBertIntermediate
modeling.py:370
↓ 1 callersClassBertLayer
modeling.py:399
↓ 1 callersClassBertOnlyMLMHead
modeling.py:481
↓ 1 callersClassBertOnlyNSPHead
modeling.py:491
↓ 1 callersClassBertOutput
modeling.py:385
↓ 1 callersClassBertPooler
modeling.py:430
↓ 1 callersClassBertPreTrainingHeads
modeling.py:501
↓ 1 callersClassBertPredictionHeadTransform
modeling.py:445
↓ 1 callersClassBertSelfAttention
modeling.py:293
↓ 1 callersClassBertSelfOutput
modeling.py:344
↓ 1 callersClassInferCoQA
infer.py:94
↓ 1 callersClassInputFeatures
A single set of features of data.
infer_utils.py:90
ClassBertForMaskedLM
BERT model with the masked language modeling head. This module comprises the BERT model followed by the masked language modeling head. Params
modeling.py:822
ClassBertForMultipleChoice
BERT model for multiple choice tasks. This module is composed of the BERT model with a linear layer on top of the pooled output. Params:
modeling.py:1011
ClassBertForNextSentencePrediction
BERT model with next sentence prediction head. This module comprises the BERT model followed by the next sentence classification head. Params
modeling.py:883
ClassBertForPreTraining
BERT model with pre-training heads. This module comprises the BERT model followed by the two pre-training heads: - the masked language mod
modeling.py:751
ClassBertForQuestionAnswering
BERT model for Question Answering (span extraction). This module is composed of the BERT model with a linear layer on top of the sequence outp
modeling.py:1153
ClassBertForSequenceClassification
BERT model for classification. This module is composed of the BERT model with a linear layer on top of the pooled output. Params:
modeling.py:945
ClassBertForTokenClassification
BERT model for token-level classification. This module is composed of the BERT model with a linear layer on top of the full hidden state of th
modeling.py:1080
ClassBertPreTrainedModel
An abstract class to handle weights initialization and a simple interface for dowloading and loading pretrained models.
modeling.py:513