Instructions to use rouadel30/t5_bbc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rouadel30/t5_bbc with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rouadel30/t5_bbc") model = AutoModelForSeq2SeqLM.from_pretrained("rouadel30/t5_bbc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t5_bbc
This model is a fine-tuned version of google-t5/t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6510
- Rouge-1: 0.4973
- Rouge-2: 0.3340
- Rouge-l: 0.3564
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l |
|---|---|---|---|---|---|---|
| 0.8185 | 1.0 | 240 | 0.7046 | 0.4676 | 0.3037 | 0.3320 |
| 0.7089 | 2.0 | 480 | 0.6732 | 0.4846 | 0.3228 | 0.3535 |
| 0.7322 | 3.0 | 720 | 0.6596 | 0.4872 | 0.3219 | 0.3517 |
| 0.6861 | 4.0 | 960 | 0.6529 | 0.4921 | 0.3280 | 0.3537 |
| 0.6968 | 5.0 | 1200 | 0.6510 | 0.4973 | 0.3340 | 0.3564 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Base model
google-t5/t5-small