An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
Paper • 1909.02027 • Published
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("marice/bert-base-uncased-finetuned-clinc")
model = AutoModelForSequenceClassification.from_pretrained("marice/bert-base-uncased-finetuned-clinc", device_map="auto")Quick Links
Intent Detection with BERT
This model was trained on the CLINC150 dataset for customer intent detection. The dataset can be found on the Hub. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marice/bert-base-uncased-finetuned-clinc")