Summarization
Transformers
PyTorch
Safetensors
bart
text2text-generation
distilbart
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("DeepNLP-22-23/MLQ-distilbart-bbc")
model = AutoModelForSeq2SeqLM.from_pretrained("DeepNLP-22-23/MLQ-distilbart-bbc")Quick Links
MLQ-distilbart-bbc
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the BBC News Summary dataset (https://www.kaggle.com/pariza/bbc-news-summary).
The model has been generated as part of the in-lab practice of Deep NLP course currently held at Politecnico di Torino.
Training parameters:
num_train_epochs=2fp16=Trueper_device_train_batch_size=1warmup_steps=10weight_decay=0.01max_seq_length=100
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sshleifer/distilbart-cnn-12-6Evaluation results
- ROUGE-2 on bbctest set self-reported61.430
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="DeepNLP-22-23/MLQ-distilbart-bbc")