Instructions to use dflcmu/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dflcmu/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dflcmu/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dflcmu/results") model = AutoModelForSequenceClassification.from_pretrained("dflcmu/results") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dflcmu/results")
model = AutoModelForSequenceClassification.from_pretrained("dflcmu/results")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dflcmu/results")