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import torch
from transformers import DistilBertTokenizerFast, DistilBertForTokenClassification
# Define label mappings (ensure this matches the mappings used during training)
label2id = {'<negative_object>': 0, 'other': 2, '<positive_object>': 1}
id2label = {v: k for k, v in label2id.items()}
def prepare_input(tokens, tokenizer, max_length=128):
encoding = tokenizer(
tokens,
is_split_into_words=True,
return_tensors="pt",
padding='max_length',
truncation=True,
max_length=max_length,
return_offsets_mapping=True
)
return encoding
def split_sentence(sentence):
# List of special tokens to preserve
special_tokens = ['<positive_object>', '<negative_object>']
# More comprehensive list of punctuation marks and symbols
punctuation = ',.?!;:()[]{}""\'`@#$%^&*+=|\\/<>-—–'
# Initialize result list and temporary word
result = []
current_word = ''
i = 0
while i < len(sentence):
# Check for special tokens
found_special = False
for token in special_tokens:
if sentence[i:].startswith(token):
# Add previous word if exists
if current_word:
result.append(current_word)
current_word = ''
# Add special token
result.append(token)
i += len(token)
found_special = True
break
if found_special:
continue
# Handle punctuation
if sentence[i] in punctuation:
# Add previous word if exists
if current_word:
result.append(current_word)
current_word = ''
# Add punctuation as separate token
result.append(sentence[i])
# Handle spaces
elif sentence[i].isspace():
if current_word:
result.append(current_word)
current_word = ''
# Build regular words
else:
current_word += sentence[i]
i += 1
# Add final word if exists
if current_word:
result.append(current_word)
return result
def predict(tokens, model, tokenizer, device, max_length=128):
tokens = split_sentence(' '.join(tokens.lower().split()))
# Prepare the input
encoding = prepare_input(tokens, tokenizer, max_length=max_length)
word_ids = encoding.word_ids(batch_index=0) # List of word IDs
# Move tensors to device
input_ids = encoding['input_ids'].to(device)
attention_mask = encoding['attention_mask'].to(device)
# Inference
with torch.no_grad():
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1).cpu().numpy()[0]
# Decode tokens and labels
tokens_decoded = tokenizer.convert_ids_to_tokens(input_ids.cpu().numpy()[0])
labels = [id2label.get(pred, 'O') for pred in predictions]
# Align tokens with original word-level tokens
aligned_predictions = []
previous_word_idx = None
for token, label, word_idx in zip(tokens_decoded, labels, word_ids):
if word_idx is None:
continue
if word_idx != previous_word_idx:
aligned_predictions.append((tokens[word_idx], label))
previous_word_idx = word_idx
return aligned_predictions
def load_token_classifier(pretrained_token_classifier_path, device):
# Load tokenizer and model
tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_token_classifier_path)
token_classifier = DistilBertForTokenClassification.from_pretrained(pretrained_token_classifier_path)
token_classifier.to(device)
return token_classifier, tokenizer |