Text Classification
Transformers
Safetensors
English
modernbert
oncology
clinical-trials
eligibility
binary-classification
text-matching
text-embeddings-inference
Instructions to use ksg-dfci/BoilerplateChecker-1225 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ksg-dfci/BoilerplateChecker-1225 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ksg-dfci/BoilerplateChecker-1225")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ksg-dfci/BoilerplateChecker-1225") model = AutoModelForSequenceClassification.from_pretrained("ksg-dfci/BoilerplateChecker-1225", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ksg-dfci/BoilerplateChecker-1225: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/ksg-dfci/BoilerplateChecker-1225/resolve/main/training_args.bin
- Command line
-
hf download hf://ksg-dfci/BoilerplateChecker-1225/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ksg-dfci/BoilerplateChecker-1225/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 49ddc9ac96ae66db1760762823673ad31d3d4f3acfdc339bd7d0561282ffebca
- Size of remote file:
- 5.78 kB
- SHA256:
- fa2f650e1117d2fc23bc6f317673608f24701efc0843ad043d322fb45857888b
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