Summarization
Transformers
PyTorch
TensorBoard
bart
text2text-generation
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("cs608/multi-news-model")
model = AutoModelForSeq2SeqLM.from_pretrained("cs608/multi-news-model")Quick Links
CS685-text-summarizer
This model is a fine-tuned version of facebook/bart-base on the multi_news dataset. It achieves the following results on the evaluation set:
- Loss: 2.4960
- Rouge1: 9.8777
- Rouge2: 3.6509
- Rougel: 7.597
- Rougelsum: 9.0495
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 2.8402 | 1.0 | 1349 | 2.5256 | 9.7548 | 3.5429 | 7.5078 | 8.9174 |
| 2.5581 | 2.0 | 2698 | 2.5011 | 9.7256 | 3.6262 | 7.5644 | 8.8904 |
| 2.3908 | 3.0 | 4047 | 2.4948 | 9.7569 | 3.5976 | 7.5209 | 8.8919 |
| 2.2744 | 4.0 | 5396 | 2.4875 | 9.9116 | 3.7758 | 7.6823 | 9.0881 |
| 2.1908 | 5.0 | 6745 | 2.4960 | 9.8777 | 3.6509 | 7.597 | 9.0495 |
Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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Dataset used to train cs608/multi-news-model
Evaluation results
- Rouge1 on multi_newsself-reported9.878
# 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="cs608/multi-news-model")