Instructions to use interneuronai/real_estate_listing_analysis_bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/real_estate_listing_analysis_bart with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/real_estate_listing_analysis_bart")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/real_estate_listing_analysis_bart") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/real_estate_listing_analysis_bart") - Notebooks
- Google Colab
- Kaggle
Real Estate Listing Analysis
Description: Perform various tasks to analyze real estate listings, including categorizing them by type, determining if they are for new buildings or business centers, identifying amenities, classifying listings based on location, and analyzing pricing trends.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/real_estate_listing_analysis_bart"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
return predictions.item()
text = "Your text here"
print("Category:", classify_text(text))
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