Spaces:
Sleeping
Sleeping
File size: 2,045 Bytes
9b562d8 9717ed1 9b562d8 |
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 |
import torch
from transformers import BertTokenizerFast, BertForTokenClassification
import gradio as gr
# Load tokenizer and model
tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
model = BertForTokenClassification.from_pretrained('maximuspowers/bias-detection-ner')
model.eval()
model.to('cuda' if torch.cuda.is_available() else 'cpu')
# Define label mappings
id2label = {
0: 'O',
1: 'B-STEREO',
2: 'I-STEREO',
3: 'B-GEN',
4: 'I-GEN',
5: 'B-UNFAIR',
6: 'I-UNFAIR'
}
def predict_ner_tags(sentence):
inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
input_ids = inputs['input_ids'].to(model.device)
attention_mask = inputs['attention_mask'].to(model.device)
with torch.no_grad():
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
probabilities = torch.sigmoid(logits)
predicted_labels = (probabilities > 0.5).int()
result = []
tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
for i, token in enumerate(tokens):
if token not in tokenizer.all_special_tokens:
label_indices = (predicted_labels[0][i] == 1).nonzero(as_tuple=False).squeeze(-1)
labels = [id2label[idx.item()] for idx in label_indices] if label_indices.numel() > 0 else ['O']
result.append((token, labels))
return result
def format_output(result):
formatted_output = ""
for token, labels in result:
formatted_output += f"{token}: {', '.join(labels)}\n"
return formatted_output
iface = gr.Interface(
fn=predict_ner_tags,
inputs="text",
outputs="text",
title="Named Entity Recognition with BERT",
description="Enter a sentence to predict NER tags using BERT model trained for multi-label classification.",
examples=["Tall men are so clumsy."],
allow_flagging="never",
interpretation="default",
postprocessing_fn=format_output
)
if __name__ == "__main__":
iface.launch()
|