import grug
from grug.backends.modern import ModernBackend

comp = grug.Compressor(ModernBackend(model_name='akshayballal/grug-ettin-68m-meetingbank-v2'))
result = comp.compress(text, rate=0.4)

Training

json

{
  "model_name": "jhu-clsp/ettin-encoder-68m",
  "epochs": 6,
  "learning_rate": 1e-05,
  "batch_size": 32,
  "max_length": 512,
  "weight_decay": 0.01,
  "warmup_ratio": 0.06,
  "device": "cuda",
  "seed": 0,
  "push_to": "akshayballal/grug-ettin-68m-meetingbank-v2",
  "select_on": "val_f1",
  "patience": 2,
  "cs_weight": 0.0
}

Final epoch

json

{
  "epoch": 6,
  "train_loss": 0.32849070216068116,
  "val_loss": 0.3391664388044825,
  "val_accuracy": 0.848027204080612,
  "val_precision": 0.8069981607731251,
  "val_recall": 0.7997938874754437,
  "val_f1": 0.8033798734488425,
  "val_perplexity": 1.4037769687306128
}

The training corpus is CC-BY-NC-SA-4.0, so this model inherits a non-commercial constraint.