Instructions to use Southstreamer/design_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Southstreamer/design_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Southstreamer/design_extractor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Southstreamer/design_extractor") model = AutoModelForSequenceClassification.from_pretrained("Southstreamer/design_extractor") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Southstreamer/design_extractor")
model = AutoModelForSequenceClassification.from_pretrained("Southstreamer/design_extractor")Quick Links
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Check out the documentation for more information.
Design Extractor
Fine-tuned on DistilBert for design meeting sentence classification. Trained on a small dataset consisting of six different one hour meetings between different software engineering groups (Jolak et al., 2018).
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Southstreamer/design_extractor")