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import json
import torch
from transformers import BertTokenizerFast, BertForTokenClassification
import gradio as gr
# init important things
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')
# ids to labels we want to display
id2label = {
0: 'O',
1: 'B-STEREO',
2: 'I-STEREO',
3: 'B-GEN',
4: 'I-GEN',
5: 'B-UNFAIR',
6: 'I-UNFAIR'
}
# predict function you'll want to use if using in your own code
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() # remember to try your own threshold
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": token, "labels": labels})
return json.dumps(result, indent=4)
# startup gradio
iface = gr.Interface(
fn=predict_ner_tags,
inputs="text",
outputs="text",
title="Social Bias Named Entity Recognition (with BERT) 🕵",
description=("Enter a sentence to predict biased parts of speech tags. This model uses multi-label BertForTokenClassification, to label the entities: (GEN)eralizations, (UNFAIR)ness, and (STEREO)types. Labels follow BIO format. Try it out :)."
"<br><br>Read more about how this model was trained in this <a href='https://huggingface.co/blog/maximuspowers/bias-entity-recognition' target='_blank'>blog post</a>."
"<br>Model Page: <a href='https://huggingface.co/maximuspowers/bias-detection-ner' target='_blank'>Bias Detection NER</a>."),
allow_flagging="never"
)
if __name__ == "__main__":
iface.launch() |