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import torch
import torch.nn as nn
import lightning as L
import torchmetrics as tm
from tokenizers import Tokenizer
import gradio as gr
from huggingface_hub import hf_hub_download
COARSE_LABELS = [
"ABBR (0): Abbreviation",
"ENTY (1): Entity",
"DESC (2): Description and abstract concept",
"HUM (3): Human being",
"LOC (4): Location",
"NUM (5): Numeric value",
]
FINE_LABELS = [
"ABBR (0): Abbreviation",
"ABBR (1): Expression abbreviated",
"ENTY (2): Animal",
"ENTY (3): Organ of body",
"ENTY (4): Color",
"ENTY (5): Invention, book and other creative piece",
"ENTY (6): Currency name",
"ENTY (7): Disease and medicine",
"ENTY (8): Event",
"ENTY (9): Food",
"ENTY (10): Musical instrument",
"ENTY (11): Language",
"ENTY (12): Letter like a-z",
"ENTY (13): Other entity",
"ENTY (14): Plant",
"ENTY (15): Product",
"ENTY (16): Religion",
"ENTY (17): Sport",
"ENTY (18): Element and substance",
"ENTY (19): Symbols and sign",
"ENTY (20): Techniques and method",
"ENTY (21): Equivalent term",
"ENTY (22): Vehicle",
"ENTY (23): Word with a special property",
"DESC (24): Definition of something",
"DESC (25): Description of something",
"DESC (26): Manner of an action",
"DESC (27): Reason",
"HUM (28): Group or organization of persons",
"HUM (29): Individual",
"HUM (30): Title of a person",
"HUM (31): Description of a person",
"LOC (32): City",
"LOC (33): Country",
"LOC (34): Mountain",
"LOC (35): Other location",
"LOC (36): State",
"NUM (37): Postcode or other code",
"NUM (38): Number of something",
"NUM (39): Date",
"NUM (40): Distance, linear measure",
"NUM (41): Price",
"NUM (42): Order, rank",
"NUM (43): Other number",
"NUM (44): Lasting time of something",
"NUM (45): Percent, fraction",
"NUM (46): Speed",
"NUM (47): Temperature",
"NUM (48): Size, area and volume",
"NUM (49): Weight",
]
class Classifier:
def __init__(self, tokenizer_ckpt_path, model_ckpt_path):
self.tokenizer = Tokenizer.from_file(tokenizer_ckpt_path)
self.model = LSTMWithAttentionClassifier.load_from_checkpoint(
model_ckpt_path,
map_location="cpu",
)
def predict(self, text):
encoding = self.tokenizer.encode(text)
ids = torch.tensor([encoding.ids])
logits, _ = self.model(ids)
probs = torch.softmax(logits, dim=1).squeeze().tolist()
return {
category: prob
for category, prob in zip(
FINE_LABELS if self.model.fine else COARSE_LABELS, probs
)
}
class Attention(nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.WQuery = nn.Linear(hidden_dim, hidden_dim)
self.WKey = nn.Linear(hidden_dim, hidden_dim)
self.WValue = nn.Linear(hidden_dim, 1)
def forward(self, x):
query = torch.tanh(self.WQuery(x))
key = torch.tanh(self.WKey(x))
attention_weights = torch.softmax(self.WValue(query + key), dim=1)
return (attention_weights * x).sum(dim=1), attention_weights
class LSTMWithAttentionClassifier(L.LightningModule):
def __init__(
self,
vocab_size,
embedding_dim,
hidden_dim,
num_classes,
lr=1e-3,
weight_decay=1e-2,
num_layers=1,
bidirectional=False,
dropout=0.0,
padding_idx=3,
fine=False,
**kwargs,
):
super().__init__()
self.save_hyperparameters()
self.lr = lr
self.weight_decay = weight_decay
self.fine = fine
self.embedding = nn.Embedding(
vocab_size,
embedding_dim,
padding_idx=padding_idx,
)
self.lstm = nn.LSTM(
embedding_dim,
hidden_dim,
num_layers=num_layers,
batch_first=True,
bidirectional=bidirectional,
dropout=dropout,
)
self.attention = Attention(
hidden_dim * (1 + bidirectional),
)
self.fc = nn.Linear(
hidden_dim * (1 + bidirectional),
num_classes,
)
self.criteria = nn.CrossEntropyLoss()
self.accuracy = tm.Accuracy(
task="multiclass",
num_classes=num_classes,
)
def forward(self, input_ids):
x = self.embedding(input_ids)
x, _ = self.lstm(x)
x, attention_weights = self.attention(x)
x = self.fc(x)
return x, attention_weights
def training_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
coarse = batch["coarse"]
fine = batch["fine"]
logits, _ = self(input_ids)
loss = self.criteria(logits, fine if self.fine else coarse)
self.log("train_loss", loss)
return loss
def validation_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
coarse = batch["coarse"]
fine = batch["fine"]
logits, _ = self(input_ids)
loss = self.criteria(logits, fine if self.fine else coarse)
self.log("val_loss", loss)
pred = logits.argmax(dim=1)
self.accuracy(pred, fine if self.fine else coarse)
self.log("val_acc", self.accuracy, prog_bar=True)
def configure_optimizers(self):
return torch.optim.AdamW(
self.parameters(),
lr=self.lr,
weight_decay=self.weight_decay,
)
tokenizer_ckpt_path = hf_hub_download(
repo_id="SatwikKambham/trec-classifier",
filename="tokenizer.json",
)
model_ckpt_path = hf_hub_download(
repo_id="SatwikKambham/trec-classifier",
filename="lstm_attention.ckpt",
)
classifier = Classifier(tokenizer_ckpt_path, model_ckpt_path)
interface = gr.Interface(
fn=classifier.predict,
inputs=gr.components.Textbox(
label="Question",
placeholder="Enter a question here...",
),
outputs=gr.components.Label(
label="Predicted class",
num_top_classes=3,
),
examples=[
[
"What does LOL mean?",
],
[
"What is the meaning of life?",
],
[
"How long does it take for light from the sun to reach the earth?",
],
[
"When is friendship day?",
],
],
)
interface.launch()
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