Instructions to use tradero/distilbert-user-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tradero/distilbert-user-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tradero/distilbert-user-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tradero/distilbert-user-intent") model = AutoModelForSequenceClassification.from_pretrained("tradero/distilbert-user-intent") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 49241119078
- CO2 Emissions (in grams): 0.4690
Validation Metrics
- Loss: 1.676
- Accuracy: 0.800
- Macro F1: 0.733
- Micro F1: 0.800
- Weighted F1: 0.733
- Macro Precision: 0.700
- Micro Precision: 0.800
- Weighted Precision: 0.700
- Macro Recall: 0.800
- Micro Recall: 0.800
- Weighted Recall: 0.800
Usage
Python API:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("tradero/distilbert-user-intent", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("tradero/distilbert-user-intent", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
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