Edit model card

Hyperparameters

learning_rate=2e-5
per_device_train_batch_size=14
per_device_eval_batch_size=14
weight_decay=0.01
save_total_limit=3
num_train_epochs=3
predict_with_generate=True
fp16=True

Training Output

global_step=7710,
training_loss=2.436398018566087,
metrics={'train_runtime': 30287.1254,
'train_samples_per_second': 3.564,
'train_steps_per_second': 0.255,
'total_flos': 3.1186278368988365e+17,
'train_loss': 2.436398018566087,
'epoch': 3.0}

Training Results

Epoch Training Loss Validation Loss Rouge1 Rouge2 Rougel Rougelsum Bleu Gen Len
1 2.451200 2.291708 0.322800 0.110100 0.194600 0.194700 0.368400 150.224300
2 2.527300 nan 0.296400 0.100100 0.181800 0.181900 0.317300 137.569200
3 2.523800 nan 0.296600 0.100000 0.181800 0.181900 0.317200 137.254000
Downloads last month
11
Safetensors
Model size
571M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train usakha/Pegasus_multiNews_model