Spaces:
Runtime error
Runtime error
File size: 6,266 Bytes
ff8f746 |
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 |
from transformers import get_scheduler
import torch
from torch.utils.data import DataLoader
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import DataCollatorForTokenClassification
from accelerate import Accelerator
import evaluate
import datasets
from tqdm.auto import tqdm
ner_tags = {
"O": 0,
"B-Rating": 1,
"I-Rating": 2,
"B-Amenity": 3,
"I-Amenity": 4,
"B-Location": 5,
"I-Location": 6,
"B-Restaurant_Name": 7,
"I-Restaurant_Name": 8,
"B-Price": 9,
"B-Hours": 10,
"I-Hours": 11,
"B-Dish": 12,
"I-Dish": 13,
"B-Cuisine": 14,
"I-Price": 15,
"I-Cuisine": 16,
}
label_names = {v: k for k, v in ner_tags.items()}
# dataset aggregation
dataset = load_dataset("tner/mit_restaurant")
dataset["train"] = datasets.concatenate_datasets([dataset["train"], dataset["validation"]])
dataset["train"] = datasets.concatenate_datasets([dataset["train"], dataset["test"]])
print(dataset)
tokenizer = AutoTokenizer.from_pretrained(
'sentence-transformers/all-MiniLM-L6-v2')
def align_labels_with_tokens(labels, word_ids):
new_labels = []
current_word = None
for word_id in word_ids:
if word_id != current_word:
# Start of a new word!
current_word = word_id
label = -100 if word_id is None else labels[word_id]
new_labels.append(label)
elif word_id is None:
# Special token
new_labels.append(-100)
else:
# Same word as previous token
label = labels[word_id]
# If the label is B-XXX we change it to I-XXX
label_name = label_names[label]
if label_name.startswith("B"):
label = ner_tags["I" + label_name[1:]]
new_labels.append(label)
return new_labels
def tokenize_and_align_labels(examples):
tokenized_inputs = tokenizer(
examples["tokens"], truncation=True, is_split_into_words=True
)
all_labels = examples["tags"]
new_labels = []
for i, labels in enumerate(all_labels):
word_ids = tokenized_inputs.word_ids(i)
new_labels.append(align_labels_with_tokens(labels, word_ids))
tokenized_inputs["labels"] = new_labels
return tokenized_inputs
tokenized_datasets = dataset.map(
tokenize_and_align_labels,
batched=True,
remove_columns=dataset["train"].column_names,
)
def train():
metric = evaluate.load("seqeval")
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
train_dataloader = DataLoader(
tokenized_datasets["train"],
shuffle=True,
collate_fn=data_collator,
batch_size=128,
)
eval_dataloader = DataLoader(
tokenized_datasets["test"],
collate_fn=data_collator,
batch_size=8
)
model = AutoModelForTokenClassification.from_pretrained(
'sentence-transformers/all-MiniLM-L6-v2',
id2label=label_names,
label2id=ner_tags,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)
accelerator = Accelerator()
model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
model, optimizer, train_dataloader, eval_dataloader
)
num_train_epochs = 50
num_update_steps_per_epoch = len(train_dataloader)
num_training_steps = num_train_epochs * num_update_steps_per_epoch
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps,
)
def postprocess(predictions, labels):
predictions = predictions.detach().cpu().clone().numpy()
labels = labels.detach().cpu().clone().numpy()
# Remove ignored index (special tokens) and convert to labels
true_labels = [[label_names[l] for l in label if l != -100]
for label in labels]
true_predictions = [
[label_names[p] for (p, l) in zip(prediction, label) if l != -100]
for prediction, label in zip(predictions, labels)
]
return true_labels, true_predictions
progress_bar = tqdm(range(num_training_steps))
for epoch in range(num_train_epochs):
# Training
model.train()
for batch in train_dataloader:
outputs = model(**batch)
loss = outputs.loss
accelerator.backward(loss)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
# Evaluation
model.eval()
for batch in eval_dataloader:
with torch.no_grad():
outputs = model(**batch)
predictions = outputs.logits.argmax(dim=-1)
labels = batch["labels"]
# Necessary to pad predictions and labels for being gathered
predictions = accelerator.pad_across_processes(
predictions, dim=1, pad_index=-100)
labels = accelerator.pad_across_processes(
labels, dim=1, pad_index=-100)
predictions_gathered = accelerator.gather(predictions)
labels_gathered = accelerator.gather(labels)
true_predictions, true_labels = postprocess(
predictions_gathered, labels_gathered)
metric.add_batch(predictions=true_predictions,
references=true_labels)
results = metric.compute()
print(
f"epoch {epoch}:",
{
key: results[f"overall_{key}"]
for key in ["precision", "recall", "f1", "accuracy"]
},
)
output_dir = "restaurant_ner"
# Save and upload
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
output_dir, save_function=accelerator.save)
if accelerator.is_main_process:
tokenizer.save_pretrained(output_dir)
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(output_dir, save_function=accelerator.save)
train() |