Instructions to use akira1608/BART-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akira1608/BART-model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("akira1608/BART-model") model = AutoModelForSeq2SeqLM.from_pretrained("akira1608/BART-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from akira1608/BART-model: direct link, hf CLI and curl.
- Browser
- Download file 1.9 kB
-
https://huggingface.co/akira1608/BART-model/resolve/main/README.md
- Command line
-
hf download hf://akira1608/BART-model/README.md
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curl -L -o README.md https://huggingface.co/akira1608/BART-model/resolve/main/README.md
1.9 kB
| base_model: amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: BART-model | |
| results: [] | |
| <!-- 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. --> | |
| # BART-model | |
| This model is a fine-tuned version of [amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2](https://huggingface.co/amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9435 | |
| ## 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: 10 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.4132 | 0.99 | 16 | 1.1373 | | |
| | 1.3997 | 1.98 | 32 | 1.1288 | | |
| | 1.3814 | 2.97 | 48 | 1.1147 | | |
| | 1.5235 | 3.95 | 64 | 1.0936 | | |
| | 1.1194 | 4.94 | 80 | 1.0703 | | |
| | 1.356 | 5.99 | 97 | 1.0459 | | |
| | 1.2157 | 6.98 | 113 | 1.0206 | | |
| | 0.9685 | 7.97 | 129 | 0.9902 | | |
| | 1.2886 | 8.96 | 145 | 0.9639 | | |
| | 1.2373 | 9.88 | 160 | 0.9435 | | |
| ### Framework versions | |
| - Transformers 4.33.0 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.4 | |
| - Tokenizers 0.13.3 | |