Instructions to use gplsi/Constructive_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gplsi/Constructive_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gplsi/Constructive_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gplsi/Constructive_model") model = AutoModelForSequenceClassification.from_pretrained("gplsi/Constructive_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_progress_scores.csv from gplsi/Constructive_model: direct link, hf CLI and curl.
- Browser
- Download file 366 Bytes
-
https://huggingface.co/gplsi/Constructive_model/resolve/main/training_progress_scores.csv
- Command line
-
hf download hf://gplsi/Constructive_model/training_progress_scores.csv
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curl -L -o training_progress_scores.csv https://huggingface.co/gplsi/Constructive_model/resolve/main/training_progress_scores.csv
366 Bytes
| global_step,train_loss,mcc,tp,tn,fp,fn,auroc,auprc,f1,acc,eval_loss | |
| 103,0.3311375081539154,0.47467085330624703,94,219,51,47,0.8124770160231154,0.7196204958185641,0.7372534182235674,0.7615571776155717,0.5319613453287345 | |
| 206,0.482230007648468,0.4798384683636588,89,227,43,52,0.815471499868663,0.7301024496763568,0.7394863788306412,0.7688564476885644,0.547819315814055 | |