Text Classification
Transformers
Safetensors
English
bert
proposal-analysis
business
binary-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use JonahDelman/ProposalClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JonahDelman/ProposalClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JonahDelman/ProposalClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JonahDelman/ProposalClassifier") model = AutoModelForSequenceClassification.from_pretrained("JonahDelman/ProposalClassifier") - Notebooks
- Google Colab
- Kaggle
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Model tree for JonahDelman/ProposalClassifier
Base model
google-bert/bert-base-uncasedEvaluation results
- accuracy on Business Proposalsself-reported0.920
- f1 on Business Proposalsself-reported0.920
- precision on Business Proposalsself-reported0.920
- recall on Business Proposalsself-reported0.920
- loss on Business Proposalsself-reported0.320