File size: 7,660 Bytes
b1e8fe0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
import os
import logging
import torch
from torch.utils.data import Dataset
from datasets import load_dataset, load_from_disk
import pandas as pd
import nltk
from config import MODEL_NAME, MAX_LENGTH, OVERLAP, PREPROCESSED_DIR, tokenizer, nlp
# =============================
# Logging Setup
# =============================
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
# =============================
# One-Time Preprocessing
# =============================
def process_data():
if not os.path.exists(PREPROCESSED_DIR):
logging.info("Preprocessing data... This may take a while.")
# Load and filter SNLI
snli = load_dataset("snli")
snli = snli.filter(lambda x: x["label"] != -1)
def build_dependency_graph(sentence):
doc = nlp(sentence)
tokens = [tok.text for tok in doc]
edges = []
for tok in doc:
if tok.head.i != tok.i:
edges.extend([(tok.i, tok.head.i), (tok.head.i, tok.i)])
return tokens, edges
def preprocess(examples):
premises = examples["premise"]
hypotheses = examples["hypothesis"]
labels = examples["label"]
tokenized = tokenizer(premises, hypotheses,
truncation=True, padding="max_length",
max_length=MAX_LENGTH)
tokenized["labels"] = labels
p_tokens_list, p_edges_list, p_idx_list = [], [], []
h_tokens_list, h_edges_list, h_idx_list = [], [], []
for p, h, input_ids in zip(premises, hypotheses, tokenized["input_ids"]):
p_toks, p_edges = build_dependency_graph(p)
h_toks, h_edges = build_dependency_graph(h)
wp_tokens = tokenizer.convert_ids_to_tokens(input_ids)
def align_tokens(spacy_tokens, wp_tokens):
node_indices, wp_idx = [], 1
for _ in spacy_tokens:
if wp_idx >= len(wp_tokens) - 1: break
node_indices.append(wp_idx)
wp_idx += 1
while wp_idx < len(wp_tokens) - 1 and wp_tokens[wp_idx].startswith("##"):
wp_idx += 1
return node_indices
p_idx = align_tokens(p_toks, wp_tokens)
h_idx = align_tokens(h_toks, wp_tokens)
p_tokens_list.append(p_toks)
p_edges_list.append(p_edges)
p_idx_list.append(p_idx)
h_tokens_list.append(h_toks)
h_edges_list.append(h_edges)
h_idx_list.append(h_idx)
tokenized.update({
"premise_graph_tokens": p_tokens_list,
"premise_graph_edges": p_edges_list,
"premise_node_indices": p_idx_list,
"hypothesis_graph_tokens": h_tokens_list,
"hypothesis_graph_edges": h_edges_list,
"hypothesis_node_indices": h_idx_list,
})
return tokenized
snli = snli.map(preprocess, batched=True)
snli.save_to_disk(PREPROCESSED_DIR)
logging.info(f"Preprocessing complete. Saved to {PREPROCESSED_DIR}")
else:
logging.info("Using existing preprocessed data at %s", PREPROCESSED_DIR)
def chunk_transcript(transcript_text, start_idx, end_idx, tokenizer):
encoded = tokenizer(transcript_text,
return_offsets_mapping=True,
add_special_tokens=True,
return_tensors=None,
max_length=1024,
padding=False,
truncation=False)
all_input_ids = encoded["input_ids"]
all_offsets = encoded["offset_mapping"]
chunks = []
i = 0
while i < len(all_input_ids):
chunk_ids = all_input_ids[i : i + MAX_LENGTH]
chunk_offsets = all_offsets[i : i + MAX_LENGTH]
attention_mask = [1] * len(chunk_ids)
no_span = 1
start_token, end_token = -1, -1
if start_idx >= 0 and end_idx >= 0:
for j, (off_s, off_e) in enumerate(chunk_offsets):
if off_s <= start_idx < off_e:
start_token = j
if off_s < end_idx <= off_e:
end_token = j
break
if 0 <= start_token <= end_token:
no_span = 0
else:
start_token, end_token = -1, -1
chunks.append({
"input_ids": torch.tensor(chunk_ids, dtype=torch.long),
"attention_mask": torch.tensor(attention_mask, dtype=torch.long),
"start_label": start_token,
"end_label": end_token,
"no_span_label": no_span,
})
i += (MAX_LENGTH - OVERLAP)
return chunks
class SpanExtractionChunkedDataset(Dataset):
def __init__(self, data):
self.samples = []
for item in data:
chunks = chunk_transcript(
item.get("transcript", ""),
item.get("start_idx", -1),
item.get("end_idx", -1),
tokenizer)
self.samples.extend(chunks)
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx]
def span_collate_fn(batch):
max_len = max(len(x["input_ids"]) for x in batch)
inputs, masks, starts, ends, nos = [], [], [], [], []
for x in batch:
pad = max_len - len(x["input_ids"])
inputs.append(torch.cat([x["input_ids"], torch.zeros(pad, dtype=torch.long)]).unsqueeze(0))
masks.append(torch.cat([x["attention_mask"], torch.zeros(pad, dtype=torch.long)]).unsqueeze(0))
starts.append(x["start_label"])
ends.append(x["end_label"])
nos.append(x["no_span_label"])
return {
"input_ids": torch.cat(inputs, dim=0),
"attention_mask": torch.cat(masks, dim=0),
"start_positions": torch.tensor(starts, dtype=torch.long),
"end_positions": torch.tensor(ends, dtype=torch.long),
"no_span_label": torch.tensor(nos, dtype=torch.long),
}
nltk.download('punkt')
nltk.download('punkt_tab')
class SentenceDataset(Dataset):
def __init__(self,
excel_path: str,
tokenizer,
max_length: int = 128):
df = pd.read_excel(excel_path)
self.samples = []
for _, row in df.iterrows():
transcript = str(row['Claude_Call'])
gold_sentences = row['Sel_K']
# if it's a string repr of list, eval it
if isinstance(gold_sentences, str):
gold_sentences = eval(gold_sentences)
# split into sentences
sentences = nltk.sent_tokenize(transcript)
for sent in sentences:
label = 1 if sent in gold_sentences else 0
enc = tokenizer.encode_plus(
sent,
max_length=max_length,
padding='max_length',
truncation=True,
return_tensors='pt'
)
self.samples.append({
'input_ids': enc['input_ids'].squeeze(0),
'attention_mask': enc['attention_mask'].squeeze(0),
'label': torch.tensor(label, dtype=torch.float)
})
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return self.samples[idx] |