Instructions to use LA1512/PubMed-fine-tune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LA1512/PubMed-fine-tune with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("LA1512/PubMed-fine-tune") model = AutoModelForSeq2SeqLM.from_pretrained("LA1512/PubMed-fine-tune") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of LA1512/PubMed on the pubmed-summarization 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: 0.00034
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- label_smoothing_factor: 0.04
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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