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import gradio as gr
model_card = "microsoft/mdeberta-v3-base"
finetuned_model = "MatteoFasulo/mdeberta-v3-base-subjectivity-sentiment-multilingual"
class CustomModel(PreTrainedModel):
config_class = DebertaV2Config
def __init__(self, config, sentiment_dim=3, num_labels=2, *args, **kwargs):
super().__init__(config, *args, **kwargs)
self.deberta = DebertaV2Model(config)
self.pooler = ContextPooler(config)
output_dim = self.pooler.output_dim
self.dropout = nn.Dropout(0.1)
self.classifier = nn.Linear(output_dim + sentiment_dim, num_labels)
def forward(self, input_ids, positive, neutral, negative, attention_mask=None, labels=None):
outputs = self.deberta(input_ids=input_ids, attention_mask=attention_mask)
encoder_layer = outputs[0]
pooled_output = self.pooler(encoder_layer)
# Sentiment features as a single tensor
sentiment_features = torch.stack((positive, neutral, negative), dim=1) # Shape: (batch_size, 3)
# Combine CLS embedding with sentiment features
combined_features = torch.cat((pooled_output, sentiment_features), dim=1)
# Classification head
logits = self.classifier(self.dropout(combined_features))
return {'logits': logits}
def load_tokenizer(model_name: str):
return AutoTokenizer.from_pretrained(model_name)
# Load the pre-trained model
def load_model(model_card: str, finetuned_model: str):
tokenizer = AutoTokenizer.from_pretrained(model_card)
config = DebertaV2Config.from_pretrained(
finetuned_model,
num_labels=2,
id2label={0: 'OBJ', 1: 'SUBJ'},
label2id={'OBJ': 0, 'SUBJ': 1},
output_attentions=False,
output_hidden_states=False
)
model = CustomModel(config=config, sentiment_dim=3, num_labels=2).from_pretrained(finetuned_model)
return model
def get_sentiment_values(text: str):
pipe = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment", tokenizer="cardiffnlp/twitter-xlm-roberta-base-sentiment", top_k=None)
sentiments = pipe(text)[0]
return {k:v for k,v in [(list(sentiment.values())[0], list(sentiment.values())[1]) for sentiment in sentiments]}
def predict_subjectivity(text):
sentiment_values = get_sentiment_values(text)
model = load_model(model_card, finetuned_model)
tokenizer = load_tokenizer(model_card)
inputs = tokenizer(text, padding=True, truncation=True, max_length=256, return_tensors='pt')
outputs = model(**inputs)
logits = outputs.get('logits')
predicted_class_idx = logits.argmax().item()
predicted_class = model.config.id2label[predicted_class_idx]
return predicted_class
demo = gr.Interface(
fn=predict_subjectivity,
inputs=gr.Textbox(
label='Input sentence',
placeholder='Enter a sentence from a news article',
info='Paste a sentence from a news article to determine if it is subjective or objective.'
),
outputs=gr.Text(
label="Prediction",
info="Whether the sentence is subjective or objective."
),
title='Subjectivity Detection',
description='Detect if a sentence is subjective or objective using a pre-trained model.'
theme='huggingface',
)
demo.launch() |