Text Classification
Transformers
Safetensors
English
bert
customer-feedback
aspect-based-sentiment-analysis
text-embeddings-inference
Instructions to use jiangzy1881/aspect-detection-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jiangzy1881/aspect-detection-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jiangzy1881/aspect-detection-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jiangzy1881/aspect-detection-model") model = AutoModelForSequenceClassification.from_pretrained("jiangzy1881/aspect-detection-model") - Notebooks
- Google Colab
- Kaggle
Aspect Detection Model
This model is part of a customer feedback analysis project.
Task
Detect whether a customer review contains a target aspect or aspect category.
Framework
- Hugging Face Transformers
- PyTorch
Usage
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="jiangzy1881/aspect-detection-model"
)
classifier("The food was amazing but the service was slow.")
Project Structure
This model was fine-tuned and saved from the assignment notebook workflow under:
DL_Project/models/
- Downloads last month
- 30