Instructions to use debbiesoon/longformer_summarise_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debbiesoon/longformer_summarise_large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("debbiesoon/longformer_summarise_large") model = AutoModelForSeq2SeqLM.from_pretrained("debbiesoon/longformer_summarise_large") - Notebooks
- Google Colab
- Kaggle
longformer_summarise_large
This model is a fine-tuned version of patrickvonplaten/led-large-16384-pubmed on an unknown 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 1.2.1
- Tokenizers 0.12.1
- Downloads last month
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