from transformers import pipeline

# Load summarization pipeline
model_name = "avanishd/pegasus-finetuned-samsum/"
summarizer = pipeline("summarization", model=model_name, tokenizer=model_name)

# Sample conversation
dialogue = """
John: Hey, are you free tomorrow?
Alice: I think so, why?
John: Want to catch a movie or grab lunch?
Alice: Sure, lunch sounds good. What time?
John: Let's say 1 PM at the new place downtown?
Alice: Works for me!
"""

# Generate summary
summary = summarizer(dialogue, max_length=60, min_length=15, do_sample=False)[0]['summary_text']

print("Summary:", summary)

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.5798	0.9992	920	1.4245

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1