Instructions to use jgriffi/bart_abstract_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jgriffi/bart_abstract_summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jgriffi/bart_abstract_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("jgriffi/bart_abstract_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from jgriffi/bart_abstract_summarization: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
-
https://huggingface.co/jgriffi/bart_abstract_summarization/resolve/main/README.md
- Command line
-
hf download hf://jgriffi/bart_abstract_summarization/README.md
-
curl -L -o README.md https://huggingface.co/jgriffi/bart_abstract_summarization/resolve/main/README.md
1.53 kB
metadata
license: mit
tags:
- generated_from_trainer
model-index:
- name: bart_abstract_summarization
results: []
bart_abstract_summarization
This model is a fine-tuned version of facebook/bart-large-cnn on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1852
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 |
|---|---|---|---|
| 0.0559 | 0.25 | 500 | 0.1601 |
| 0.0068 | 0.49 | 1000 | 0.2571 |
| 0.0016 | 0.74 | 1500 | 0.4330 |
| 0.0001 | 0.99 | 2000 | 0.1852 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1