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
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
Add transformer+TF-IDF ensemble (line-item top-1 0.597 -> 0.686) and document it in the card
9d4c04e verified Download requirements.txt from constructelligence/masterformat-classifier: direct link, hf CLI and curl.
- Browser
- Download file 206 Bytes
-
https://huggingface.co/constructelligence/masterformat-classifier/resolve/main/requirements.txt
- Command line
-
hf download hf://constructelligence/masterformat-classifier/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/constructelligence/masterformat-classifier/resolve/main/requirements.txt
206 Bytes
| # Core inference (PyTorch) | |
| transformers>=4.40 | |
| torch>=2.0 | |
| # ONNX path: pip install onnxruntime | |
| # onnxruntime>=1.17 | |
| # numpy>=1.24 | |
| # Ensemble (ensemble/predict_ensemble.py) | |
| # scikit-learn>=1.3 | |
| # joblib>=1.3 | |