sentiment-analysis-for-trending-topic-sentiment

pip install transformers torch

pip install transformers torch

pip install transformers torch

from transformers import BertForSequenceClassification, BertTokenizer
import torch

# Load quantized model
quantized_model_path = "AventIQ-AI/sentiment-analysis-for-trending-topic-sentiment"
quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
quantized_model.eval()  # Set to evaluation mode
quantized_model.half()  # Convert model to FP16

# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Define a test sentence
test_sentence = "The new Pixel 9 Pro has finally launched, and the reactions are pouring in. Many users are thrilled with the upgraded camera system and the sleek design, calling it the best Android phone of the year. However, some are disappointed by the high price tag and limited availability in certain countries. While tech enthusiasts appreciate the improved AI features, casual users feel the changes are too minor to justify an upgrade. Overall, the buzz around the Pixel 9 Pro is strong, with a mix of praise and criticism driving the conversation."

# Tokenize input
inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Ensure input tensors are in correct dtype
inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type
inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

# Make prediction
with torch.no_grad():
    outputs = quantized_model(**inputs)

# Get predicted class
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted Class: {predicted_class}")


label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"}  # Example

predicted_label = label_mapping[predicted_class]
print(f"Predicted Label: {predicted_label}")

from transformers import BertForSequenceClassification, BertTokenizer
import torch

# Load quantized model
quantized_model_path = "AventIQ-AI/sentiment-analysis-for-trending-topic-sentiment"
quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
quantized_model.eval()  # Set to evaluation mode
quantized_model.half()  # Convert model to FP16

# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Define a test sentence
test_sentence = "The new Pixel 9 Pro has finally launched, and the reactions are pouring in. Many users are thrilled with the upgraded camera system and the sleek design, calling it the best Android phone of the year. However, some are disappointed by the high price tag and limited availability in certain countries. While tech enthusiasts appreciate the improved AI features, casual users feel the changes are too minor to justify an upgrade. Overall, the buzz around the Pixel 9 Pro is strong, with a mix of praise and criticism driving the conversation."

# Tokenize input
inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Ensure input tensors are in correct dtype
inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type
inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

# Make prediction
with torch.no_grad():
    outputs = quantized_model(**inputs)

# Get predicted class
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted Class: {predicted_class}")


label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"}  # Example

predicted_label = label_mapping[predicted_class]
print(f"Predicted Label: {predicted_label}")

from transformers import BertForSequenceClassification, BertTokenizer
import torch

# Load quantized model
quantized_model_path = "AventIQ-AI/sentiment-analysis-for-trending-topic-sentiment"
quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
quantized_model.eval()  # Set to evaluation mode
quantized_model.half()  # Convert model to FP16

# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

# Define a test sentence
test_sentence = "The new Pixel 9 Pro has finally launched, and the reactions are pouring in. Many users are thrilled with the upgraded camera system and the sleek design, calling it the best Android phone of the year. However, some are disappointed by the high price tag and limited availability in certain countries. While tech enthusiasts appreciate the improved AI features, casual users feel the changes are too minor to justify an upgrade. Overall, the buzz around the Pixel 9 Pro is strong, with a mix of praise and criticism driving the conversation."

# Tokenize input
inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)

# Ensure input tensors are in correct dtype
inputs["input_ids"] = inputs["input_ids"].long()  # Convert to long type
inputs["attention_mask"] = inputs["attention_mask"].long()  # Convert to long type

# Make prediction
with torch.no_grad():
    outputs = quantized_model(**inputs)

# Get predicted class
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted Class: {predicted_class}")


label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"}  # Example

predicted_label = label_mapping[predicted_class]
print(f"Predicted Label: {predicted_label}")

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safensors/     # Fine Tuned Model
├── README.md            # Model documentation