Instructions to use interneuronai/customer_feedback_analysis_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/customer_feedback_analysis_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/customer_feedback_analysis_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/customer_feedback_analysis_bert") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/customer_feedback_analysis_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {} | |
| ### Customer Feedback Analysis | |
| **Description:** Classify customer feedback based on sentiment and topic to identify improvement areas and strengthen customer engagement. | |
| ## How to Use | |
| Here is how to use this model to classify text into different categories: | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| model_name = "interneuronai/customer_feedback_analysis_bert" | |
| 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)) |