Instructions to use gurpreetzenscale/bart-cnn-aps-fineTuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gurpreetzenscale/bart-cnn-aps-fineTuned with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gurpreetzenscale/bart-cnn-aps-fineTuned") model = AutoModelForSeq2SeqLM.from_pretrained("gurpreetzenscale/bart-cnn-aps-fineTuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
bart-cnn-aps-fineTuned
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: 0.0208
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: 1e-05
- 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
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 6 | 0.1899 |
| 1.3242 | 2.0 | 12 | 0.0825 |
| 1.3242 | 3.0 | 18 | 0.0546 |
| 0.069 | 4.0 | 24 | 0.0347 |
| 0.0352 | 5.0 | 30 | 0.0277 |
| 0.0352 | 6.0 | 36 | 0.0242 |
| 0.0253 | 7.0 | 42 | 0.0217 |
| 0.0253 | 8.0 | 48 | 0.0210 |
| 0.0216 | 9.0 | 54 | 0.0208 |
| 0.0201 | 10.0 | 60 | 0.0208 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for gurpreetzenscale/bart-cnn-aps-fineTuned
Base model
facebook/bart-large-cnn