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import logging
import os
import random
import numpy as np
import torch
from model import JointPhoBERT, JointXLMR
from seqeval.metrics import f1_score, precision_score, recall_score
from transformers import (
AutoTokenizer,
RobertaConfig,
XLMRobertaConfig,
XLMRobertaTokenizer,
)
MODEL_CLASSES = {
"xlmr": (XLMRobertaConfig, JointXLMR, XLMRobertaTokenizer),
"phobert": (RobertaConfig, JointPhoBERT, AutoTokenizer),
}
MODEL_PATH_MAP = {
"xlmr": "xlm-roberta-base",
"phobert": "vinai/phobert-base",
}
def get_intent_labels(args):
return [
label.strip()
for label in open(os.path.join(args.data_dir, args.token_level, args.intent_label_file), "r", encoding="utf-8")
]
def get_slot_labels(args):
return [
label.strip()
for label in open(os.path.join(args.data_dir, args.token_level, args.slot_label_file), "r", encoding="utf-8")
]
def load_tokenizer(args):
return MODEL_CLASSES[args.model_type][2].from_pretrained(args.model_name_or_path)
def init_logger():
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if not args.no_cuda and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
def compute_metrics(intent_preds, intent_labels, slot_preds, slot_labels):
assert len(intent_preds) == len(intent_labels) == len(slot_preds) == len(slot_labels)
results = {}
intent_result = get_intent_acc(intent_preds, intent_labels)
slot_result = get_slot_metrics(slot_preds, slot_labels)
sementic_result = get_sentence_frame_acc(intent_preds, intent_labels, slot_preds, slot_labels)
mean_intent_slot = (intent_result["intent_acc"] + slot_result["slot_f1"]) / 2
results.update(intent_result)
results.update(slot_result)
results.update(sementic_result)
results["mean_intent_slot"] = mean_intent_slot
return results
def get_slot_metrics(preds, labels):
assert len(preds) == len(labels)
return {
"slot_precision": precision_score(labels, preds),
"slot_recall": recall_score(labels, preds),
"slot_f1": f1_score(labels, preds),
}
def get_intent_acc(preds, labels):
acc = (preds == labels).mean()
return {"intent_acc": acc}
def read_prediction_text(args):
return [text.strip() for text in open(os.path.join(args.pred_dir, args.pred_input_file), "r", encoding="utf-8")]
def get_sentence_frame_acc(intent_preds, intent_labels, slot_preds, slot_labels):
"""For the cases that intent and all the slots are correct (in one sentence)"""
# Get the intent comparison result
intent_result = intent_preds == intent_labels
# Get the slot comparision result
slot_result = []
for preds, labels in zip(slot_preds, slot_labels):
assert len(preds) == len(labels)
one_sent_result = True
for p, l in zip(preds, labels):
if p != l:
one_sent_result = False
break
slot_result.append(one_sent_result)
slot_result = np.array(slot_result)
semantic_acc = np.multiply(intent_result, slot_result).mean()
return {"semantic_frame_acc": semantic_acc}
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