Spaces:
Sleeping
Sleeping
Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from datasets import load_dataset
|
2 |
+
|
3 |
+
dataset = load_dataset('rwcuffney/pick_a_card_test', batch_size=32, shuffle=True)
|
4 |
+
|
5 |
+
from transformers import AutoModelForSequenceClassification
|
6 |
+
|
7 |
+
model = AutoModelForSequenceClassification.from_pretrained('rwcuffney/autotrain-pick_a_card-3726099224')
|
8 |
+
|
9 |
+
from transformers import AutoTokenizer
|
10 |
+
|
11 |
+
tokenizer = AutoTokenizer.from_pretrained('rwcuffney/autotrain-pick_a_card-3726099224')
|
12 |
+
|
13 |
+
def preprocess_text(text):
|
14 |
+
encoded = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
|
15 |
+
return encoded
|
16 |
+
|
17 |
+
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
18 |
+
model.to(device)
|
19 |
+
model.eval()
|
20 |
+
|
21 |
+
for batch in dataset:
|
22 |
+
# Preprocess the text
|
23 |
+
text = batch['text']
|
24 |
+
inputs = preprocess_text(text)
|
25 |
+
inputs = inputs.to(device)
|
26 |
+
|
27 |
+
# Make predictions
|
28 |
+
with torch.no_grad():
|
29 |
+
outputs = model(**inputs)
|
30 |
+
predicted_classes = torch.argmax(outputs.logits, dim=-1)
|
31 |
+
|
32 |
+
# Print the predicted class labels
|
33 |
+
predicted_labels = [dataset.features['label'].names[i] for i in predicted_classes]
|
34 |
+
print(predicted_labels)
|
35 |
+
|
36 |
+
|
37 |
+
|