Downloads · 30 days
64
0% of all-time downloads
pegasus-samsum
transformersbook/pegasus-samsum
pegasus-samsum is a machine learning model from transformersbook. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
1.4M
Public
Repo size
4.6 GB
Likes
7
Public
Repo makeup
Click a slice to open those files.
.bin2.3 GB · 100%
At a glance
- Library
- transformers
- Model type
- pegasus
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Feb 5, 2022
- SHA
- f0017016
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
pegasus-samsum-test
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset. The model is trained in Chapter 6: Summarization in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
It achieves the following results on the evaluation set:
- Loss: 1.4875
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7012 | 0.54 | 500 | 1.4875 |
Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
- Library
- transformers
- Type
- pegasus
- Created
- Mar 2, 2022
- Updated
- Feb 5, 2022