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constructelligence
/
masterformat-classifier

Text Classification
Transformers
Joblib
ONNX
Safetensors
English
bert
masterformat
masterformat-classifier
csi-masterformat
construction
construction-technology
sequence-classification
tfidf
ensemble
specs
spec-writing
specifications
takeoff
estimating
cost-code
ufgs
public-domain
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use constructelligence/masterformat-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use constructelligence/masterformat-classifier with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="constructelligence/masterformat-classifier")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("constructelligence/masterformat-classifier")
    model = AutoModelForSequenceClassification.from_pretrained("constructelligence/masterformat-classifier", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
masterformat-classifier / ensemble
112 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
constructelligence's picture
constructelligence
Upgrade ensemble to word+char TF-IDF: line-item 0.686->0.702, section 0.613->0.620 (weights chosen on val)
ad1145a verified 2 days ago
  • blend.json
    571 Bytes
    Upgrade ensemble to word+char TF-IDF: line-item 0.686->0.702, section 0.613->0.620 (weights chosen on val) 2 days ago
  • predict_ensemble.py
    6.73 kB
    Upgrade ensemble to word+char TF-IDF: line-item 0.686->0.702, section 0.613->0.620 (weights chosen on val) 2 days ago
  • tfidf.joblib

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

    112 MB
    xet
    Upgrade ensemble to word+char TF-IDF: line-item 0.686->0.702, section 0.613->0.620 (weights chosen on val) 2 days ago