primeqa.ir.dense.colbert_top.colbert.infra.config.ColBERTConfig

class primeqa.ir.dense.colbert_top.colbert.infra.config.ColBERTConfig(ncells: int = DefaultVal(val=None), centroid_score_threshold: float = DefaultVal(val=None), ndocs: int = DefaultVal(val=None), index_path: str = DefaultVal(val=None), index_location: str = DefaultVal(val=None), nbits: int = DefaultVal(val=1), kmeans_niters: int = DefaultVal(val=20), num_partitions_max: int = DefaultVal(val=10000000), similarity: str = DefaultVal(val='cosine'), bsize: int = DefaultVal(val=32), accumsteps: int = DefaultVal(val=1), lr: float = DefaultVal(val=3e-06), maxsteps: int = DefaultVal(val=500000), save_every: int = DefaultVal(val=None), resume: bool = DefaultVal(val=False), resume_optimizer: bool = DefaultVal(val=False), warmup: int = DefaultVal(val=None), warmup_bert: int = DefaultVal(val=None), relu: bool = DefaultVal(val=False), nway: int = DefaultVal(val=2), use_ib_negatives: bool = DefaultVal(val=False), reranker: bool = DefaultVal(val=False), distillation_alpha: float = DefaultVal(val=1.0), ignore_scores: bool = DefaultVal(val=False), shuffle_every_epoch: bool = DefaultVal(val=False), save_steps: int = DefaultVal(val=2000), save_epochs: int = DefaultVal(val=- 1), epochs: int = DefaultVal(val=10), input_arguments: dict = DefaultVal(val={}), local_models_repository: str = DefaultVal(val=None), ranks_fn: str = DefaultVal(val=None), output_dir: str = DefaultVal(val=None), topK: int = DefaultVal(val=100), student_teacher_temperature: float = DefaultVal(val=1.0), student_teacher_top_loss_weight: float = DefaultVal(val=0.5), teacher_doc_maxlen: int = DefaultVal(val=180), distill_query_passage_separately: bool = DefaultVal(val=False), query_only: bool = DefaultVal(val=False), loss_function: str = DefaultVal(val=None), query_weight: float = DefaultVal(val=0.5), rng_seed: int = DefaultVal(val=12345), query_maxlen: int = DefaultVal(val=32), attend_to_mask_tokens: bool = DefaultVal(val=False), interaction: str = DefaultVal(val='colbert'), dim: int = DefaultVal(val=128), doc_maxlen: int = DefaultVal(val=180), mask_punctuation: bool = DefaultVal(val=True), checkpoint: str = DefaultVal(val=None), teacher_checkpoint: str = DefaultVal(val=None), triples: str = DefaultVal(val=None), teacher_triples: str = DefaultVal(val=None), collection: str = DefaultVal(val=None), queries: str = DefaultVal(val=None), index_name: str = DefaultVal(val=None), overwrite: bool = DefaultVal(val=False), root: str = DefaultVal(val='/home/runner/work/primeqa/primeqa/docs/experiments'), experiment: str = DefaultVal(val='default'), index_root: str = DefaultVal(val=None), name: str = DefaultVal(val='2024-08/29/18.14.15'), rank: int = DefaultVal(val=0), nranks: int = DefaultVal(val=1), amp: bool = DefaultVal(val=True), gpus: int = DefaultVal(val=0))

Bases: primeqa.ir.dense.colbert_top.colbert.infra.config.settings.RunSettings, primeqa.ir.dense.colbert_top.colbert.infra.config.settings.ResourceSettings, primeqa.ir.dense.colbert_top.colbert.infra.config.settings.DocSettings, primeqa.ir.dense.colbert_top.colbert.infra.config.settings.QuerySettings, primeqa.ir.dense.colbert_top.colbert.infra.config.settings.TrainingSettings, primeqa.ir.dense.colbert_top.colbert.infra.config.settings.IndexingSettings, primeqa.ir.dense.colbert_top.colbert.infra.config.settings.SearchSettings, primeqa.ir.dense.colbert_top.colbert.infra.config.base_config.BaseConfig

Methods

assign_defaults
configure
export
from_deprecated_args
from_existing
from_path
help
load_from_checkpoint
load_from_index
save
save_for_checkpoint
set

Attributes

accumsteps
amp
attend_to_mask_tokens
bsize
centroid_score_threshold
checkpoint
collection
device_
dim
distill_query_passage_separately
distillation_alpha
doc_maxlen
epochs
experiment
gpus
gpus_
ignore_scores
index_location
index_name
index_path
index_path_
index_root
index_root_
input_arguments
interaction
kmeans_niters
local_models_repository
loss_function
lr
mask_punctuation
maxsteps
name
nbits
ncells
ndocs
nranks
num_partitions_max
nway
output_dir
overwrite
path_
queries
query_maxlen
query_only
query_weight
rank
ranks_fn
relu
reranker
resume
resume_optimizer
rng_seed
root
save_epochs
save_every
save_steps
script_name_
shuffle_every_epoch
similarity
student_teacher_temperature
student_teacher_top_loss_weight
teacher_checkpoint
teacher_doc_maxlen
teacher_triples
topK
total_visible_gpus
triples
use_ib_negatives
warmup
warmup_bert