Instructions to use interneuronai/gym_membership_upgrades_pegasus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use interneuronai/gym_membership_upgrades_pegasus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="interneuronai/gym_membership_upgrades_pegasus")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("interneuronai/gym_membership_upgrades_pegasus") model = AutoModelForSequenceClassification.from_pretrained("interneuronai/gym_membership_upgrades_pegasus") - Notebooks
- Google Colab
- Kaggle
Gym Membership Upgrades
Description: Classify member feedback to identify potential areas of improvement and opportunities for upselling premium services, such as personal training or nutrition counseling.
How to Use
Here is how to use this model to classify text into different categories:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "interneuronai/gym_membership_upgrades_pegasus"
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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