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from typing import Any, Dict, List, Optional
import dataclasses
import glob
import os
import sys
import json
import spacy
from spacy.language import Language
from sftp import SpanPredictor
@dataclasses.dataclass
class FrameAnnotation:
tokens: List[str] = dataclasses.field(default_factory=list)
pos: List[str] = dataclasses.field(default_factory=list)
@dataclasses.dataclass
class MultiLabelAnnotation(FrameAnnotation):
frame_list: List[List[str]] = dataclasses.field(default_factory=list)
lu_list: List[Optional[str]] = dataclasses.field(default_factory=list)
def to_txt(self):
for i, tok in enumerate(self.tokens):
yield f"{tok} {self.pos[i]} {'|'.join(self.frame_list[i]) or '_'} {self.lu_list[i] or '_'}"
def convert_to_seq_labels(sentence: List[str], structures: Dict[int, Dict[str, Any]]) -> List[List[str]]:
labels = [[] for _ in sentence]
for struct_id, struct in structures.items():
tgt_span = struct["target"]
frame = struct["frame"]
for i in range(tgt_span[0], tgt_span[1] + 1):
labels[i].append(f"T:{frame}@{struct_id:02}")
for role in struct["roles"]:
role_span = role["boundary"]
role_label = role["label"]
for i in range(role_span[0], role_span[1] + 1):
prefix = "B" if i == role_span[0] else "I"
labels[i].append(f"{prefix}:{frame}:{role_label}@{struct_id:02}")
return labels
def predict_combined(
spacy_model: Language,
sentences: List[str],
tgt_predictor: SpanPredictor,
frm_predictor: SpanPredictor,
bnd_predictor: SpanPredictor,
arg_predictor: SpanPredictor,
) -> List[MultiLabelAnnotation]:
annotations_out = []
for sent_idx, sent in enumerate(sentences):
sent = sent.strip()
print(f"Processing sent with idx={sent_idx}: {sent}")
doc = spacy_model(sent)
sent_tokens = [t.text for t in doc]
tgt_spans, _, _ = tgt_predictor.force_decode(sent_tokens)
frame_structures = {}
for i, span in enumerate(tgt_spans):
span = tuple(span)
_, fr_labels, _ = frm_predictor.force_decode(sent_tokens, child_spans=[span])
frame = fr_labels[0]
if frame == "@@VIRTUAL_ROOT@@@":
continue
boundaries, _, _ = bnd_predictor.force_decode(sent_tokens, parent_span=span, parent_label=frame)
_, arg_labels, _ = arg_predictor.force_decode(sent_tokens, parent_span=span, parent_label=frame, child_spans=boundaries)
frame_structures[i] = {
"target": span,
"frame": frame,
"roles": [
{"boundary": bnd, "label": label}
for bnd, label in zip(boundaries, arg_labels)
if label != "Target"
]
}
annotations_out.append(MultiLabelAnnotation(
tokens=sent_tokens,
pos=[t.pos_ for t in doc],
frame_list=convert_to_seq_labels(sent_tokens, frame_structures),
lu_list=[None for _ in sent_tokens]
))
return annotations_out
def main(input_folder):
print("Loading spaCy model ...")
nlp = spacy.load("it_core_news_md")
print("Loading predictors ...")
zs_predictor = SpanPredictor.from_path("/data/p289731/cloned/lome-models/models/spanfinder/model.mod.tar.gz", cuda_device=0)
ev_predictor = SpanPredictor.from_path("/scratch/p289731/lome-training-files/train-evalita-plus-fn-vanilla/model.tar.gz", cuda_device=0)
print("Reading input files ...")
for file in glob.glob(os.path.join(input_folder, "*.txt")):
print(file)
with open(file, encoding="utf-8") as f:
sentences = list(f)
annotations = predict_combined(nlp, sentences, zs_predictor, ev_predictor, ev_predictor, ev_predictor)
out_name = os.path.splitext(os.path.basename(file))[0]
with open(f"../../data-out/{out_name}.combined_zs_ev.tc_bilstm.txt", "w", encoding="utf-8") as f_out:
for ann in annotations:
for line in ann.to_txt():
f_out.write(line + os.linesep)
f_out.write(os.linesep)
with open(f"../../data-out/{out_name}.combined_zs_ev.tc_bilstm.json", "w", encoding="utf-8") as f_out:
json.dump([dataclasses.asdict(ann) for ann in annotations], f_out)
if __name__ == "__main__":
main(sys.argv[1])
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