File size: 4,938 Bytes
ced4316 3133b5e d868d2e 3133b5e ced4316 3133b5e ced4316 3133b5e ced4316 e7eaeed ced4316 3133b5e ced4316 d868d2e 3133b5e ced4316 d868d2e ced4316 d868d2e ced4316 3133b5e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
import json
import logging
from typing import Iterable, Optional, Sequence
import gradio as gr
from hydra.utils import instantiate
from pie_modules.document.processing import RegexPartitioner, SpansViaRelationMerger
# this is required to dynamically load the PIE models
from pie_modules.models import * # noqa: F403
from pie_modules.taskmodules import * # noqa: F403
from pie_modules.taskmodules import PointerNetworkTaskModuleForEnd2EndRE
from pytorch_ie import Pipeline
from pytorch_ie.annotations import LabeledSpan
from pytorch_ie.documents import (
TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
)
# this is required to dynamically load the PIE models
from pytorch_ie.models import * # noqa: F403
from pytorch_ie.taskmodules import * # noqa: F403
from src.utils import parse_config
logger = logging.getLogger(__name__)
def get_merger() -> SpansViaRelationMerger:
return SpansViaRelationMerger(
relation_layer="binary_relations",
link_relation_label="parts_of_same",
create_multi_spans=True,
result_document_type=TextDocumentWithLabeledMultiSpansBinaryRelationsAndLabeledPartitions,
result_field_mapping={
"labeled_spans": "labeled_multi_spans",
"binary_relations": "binary_relations",
"labeled_partitions": "labeled_partitions",
},
combine_scores_method="product",
)
def create_document(
text: str, doc_id: str, split_regex: Optional[str] = None
) -> TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions:
"""Create a TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions from the provided
text.
Parameters:
text: The text to process.
doc_id: The ID of the document.
split_regex: A regular expression pattern to use for splitting the text into partitions.
Returns:
The processed document.
"""
document = TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions(
id=doc_id, text=text, metadata={}
)
if split_regex is not None:
partitioner = RegexPartitioner(
pattern=split_regex, partition_layer_name="labeled_partitions"
)
document = partitioner(document)
else:
# add single partition from the whole text (the model only considers text in partitions)
document.labeled_partitions.append(LabeledSpan(start=0, end=len(text), label="text"))
return document
def create_documents(
texts: Iterable[str], doc_ids: Iterable[str], split_regex: Optional[str] = None
) -> Sequence[TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions]:
"""Create a sequence of TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions from the provided
texts.
Parameters:
texts: The texts to process.
doc_ids: The IDs of the documents.
split_regex: A regular expression pattern to use for splitting the text into partitions.
Returns:
The processed documents.
"""
return [
create_document(text=text, doc_id=doc_id, split_regex=split_regex)
for text, doc_id in zip(texts, doc_ids)
]
def load_argumentation_model(config_str: str, **kwargs) -> Optional[Pipeline]:
try:
config = parse_config(config_str, format="yaml")
if config is None or config == {}:
gr.Warning("Empty argumentation model config provided. No model loaded.")
return None
# for PIE AutoPipeline, we need to handle the revision separately for
# the taskmodule and the model
if (
config.get("_target_", "").strip().endswith("AutoPipeline.from_pretrained")
and "revision" in config
):
revision = config.pop("revision")
if "taskmodule_kwargs" not in config:
config["taskmodule_kwargs"] = {}
config["taskmodule_kwargs"]["revision"] = revision
if "model_kwargs" not in config:
config["model_kwargs"] = {}
config["model_kwargs"]["revision"] = revision
model = instantiate(config, **kwargs)
gr.Info(f"Loaded argumentation model: {json.dumps({**config, **kwargs})}")
except Exception as e:
raise gr.Error(f"Failed to load argumentation model: {e}")
return model
def set_relation_types(
argumentation_model: Pipeline,
default: Optional[Sequence[str]] = None,
) -> gr.Dropdown:
if isinstance(argumentation_model.taskmodule, PointerNetworkTaskModuleForEnd2EndRE):
relation_types = argumentation_model.taskmodule.labels_per_layer["binary_relations"]
else:
raise gr.Error("Unsupported taskmodule for relation types")
return gr.Dropdown(
choices=relation_types,
label="Argumentative Relation Types",
value=default,
multiselect=True,
)
|