Instructions to use royam0820/pegasus-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use royam0820/pegasus-samsum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("royam0820/pegasus-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("royam0820/pegasus-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from royam0820/pegasus-samsum: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/royam0820/pegasus-samsum/resolve/main/README.md
- Command line
-
hf download hf://royam0820/pegasus-samsum/README.md
-
curl -L -o README.md https://huggingface.co/royam0820/pegasus-samsum/resolve/main/README.md
1.12 kB
metadata
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
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
Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1+cu113
- Datasets 2.0.0
- Tokenizers 0.10.3