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
| language: en | |
| tags: | |
| - transformers | |
| - text-classification | |
| - customer-feedback | |
| - aspect-based-sentiment-analysis | |
| pipeline_tag: text-classification | |
| # 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 | |
| ```python | |
| 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: | |
| ```text | |
| DL_Project/models/ | |
| ``` | |