Conversation
Learning
- XPM Configxpmir.conversation.learning.DatasetConversationEntrySampler(*, dataset)[source]
Bases:
BaseSamplerSubmit type:
xpmir.conversation.learning.DatasetConversationEntrySamplerUses a conversation dataset and topic records entries
- dataset: datamaestro_text.data.conversation.base.ConversationDataset
The conversation dataset
- XPM Configxpmir.conversation.learning.reformulation.ConversationRepresentationEncoder[source]
Bases:
TextEncoderBase[List[Record],RepresentationOutput],ABCSubmit type:
xpmir.conversation.learning.reformulation.ConversationRepresentationEncoder
- XPM Configxpmir.conversation.learning.reformulation.DecontextualizedQueryConverter[source]
Bases:
Converter[Record,str]Submit type:
xpmir.conversation.learning.reformulation.DecontextualizedQueryConverter
CoSPLADE
- XPM Configxpmir.conversation.models.cosplade.AsymetricMSEContextualizedRepresentationLoss(*, weight)[source]
Bases:
AlignmentLoss[CoSPLADEOutput,TextsRepresentationOutput]Submit type:
xpmir.conversation.models.cosplade.AsymetricMSEContextualizedRepresentationLossComputes the asymetric loss for CoSPLADE
- weight: float = 1.0
Weight for this loss
- XPM Configxpmir.conversation.models.cosplade.CoSPLADE(*, history_size, queries_encoder, history_encoder)[source]
Bases:
ConversationRepresentationEncoderSubmit type:
xpmir.conversation.models.cosplade.CoSPLADECoSPLADE model
- history_size: int = 0
Size of history to take into account (0 for infinite)
- queries_encoder: xpmir.neural.splade.SpladeTextEncoderV2[List[List[str]]]
Encoder for the query history (the first one being the current one)
- history_encoder: xpmir.neural.splade.SpladeTextEncoderV2[Tuple[str, str]]
Encoder for (query, answer) pairs