# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Chaconne/BDAI")
model = AutoModelForSequenceClassification.from_pretrained("Chaconne/BDAI")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Stack Exchange User Post Classification
Introduction
- This model is fine-tuned on the user posts of Stack Exchange. Stack Exchange is a Q&A community. The community has several topics and users post contents under one of those topics. This model is intended to classify a natural language input into a topic.
Usage
- Given a natural language input, the model classifies the input into one of the six lables (topics). For instance, sentencens that discuss software engineering are likely to be classified into the label "software engineering".
Note
- As this is a demo model, only a portion of the original data was used. Since there were only 6 labels in the training set, the model currently is just capable of classifying the 6 labels.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chaconne/BDAI")