wehub-resource-sync
103 lines
3.9 KiB
Python
103 lines
3.9 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass, field
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from typing import List, Optional
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@dataclass
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class ReftArgument:
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layers: str = field(default="all", metadata={"help": "Layer configuration for the model."})
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position: str = field(default="f7+l7", metadata={"help": "Position parameter for model."})
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intervention_type: str = field(default="LoreftIntervention", metadata={"help": "Type of intervention."})
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rank: int = field(default=8, metadata={"help": "Rank parameter for model."})
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act_fn: str = field(default="linear", metadata={"help": "Activation function."})
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add_bias: bool = field(default=False, metadata={"help": "Flag indicating whether to add bias."})
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dropout: float = field(default=0.0, metadata={"help": "Dropout rate."})
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@dataclass
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class GenerateArgument:
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top_k: int = field(
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default=1,
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metadata={
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"help": "The number of highest probability tokens to keep for top-k-filtering in the sampling strategy"
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},
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)
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top_p: float = field(
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default=1.0, metadata={"help": "The cumulative probability for top-p-filtering in the sampling strategy."}
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)
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@dataclass
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class EmbeddingArgument:
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max_query_len: int = field(
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default=1,
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metadata={
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"help": "The number of highest probability tokens to keep for top-k-filtering in the sampling strategy"
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},
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)
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max_passage_len: int = field(
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default=1.0, metadata={"help": "The cumulative probability for top-p-filtering in the sampling strategy."}
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)
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group_size: int = field(
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default=8,
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metadata={
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"help": (
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"Number of total positive and negative samples associated with " "each query for embedding training."
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)
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},
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)
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query_template: str = field(
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default="Query: {text}\nUse one word to summarize the query's relevant information. The word is: \"",
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metadata={
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"help": (
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"Query template. Ensure the template includes the placeholder "
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"'{text}' to insert the actual query text."
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)
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},
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)
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passage_template: str = field(
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default="Text: {text}\nUse one word to summarize the text's content. The word is: \"",
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metadata={
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"help": (
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"Passage template. Ensure the template includes the placeholder "
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"'{text}' to insert the actual passage text."
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)
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},
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)
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return_position_ids: bool = field(
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default=True,
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metadata={"help": "Whether to return position ids for each sentence."},
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)
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embedding_temperature: float = field(
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default=0.02,
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metadata={"help": "The temperature used in embedding learning."},
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)
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embedding_negatives_cross_device: bool = field(
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default=True,
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metadata={"help": "Whether to share the negatives across all GPUs."},
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)
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embedding_matryoshka_dims: Optional[List[int]] = field(
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default=None,
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metadata={"help": "The dims for matryoshka training."},
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)
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loss_type: str = field(
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default="contrastive",
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metadata={"help": "The type of loss computation."},
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)
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inf_cl_head_dim: int = field(
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default=64,
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metadata={"help": "The size of the head dimension when gpu ops are set as 'inf_cl'."},
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)
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