Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Ash11/cnn_news_summary_model_trained_on_reduced_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ash11/cnn_news_summary_model_trained_on_reduced_data with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ash11/cnn_news_summary_model_trained_on_reduced_data") model = AutoModelForSeq2SeqLM.from_pretrained("Ash11/cnn_news_summary_model_trained_on_reduced_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
cnn_news_summary_model_trained_on_reduced_data
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6040
- Rouge1: 0.2179
- Rouge2: 0.0942
- Rougel: 0.1839
- Rougelsum: 0.1839
- Generated Length: 19.0
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 431 | 1.6239 | 0.2175 | 0.0937 | 0.1829 | 0.183 | 19.0 |
| 1.92 | 2.0 | 862 | 1.6075 | 0.2169 | 0.0936 | 0.1828 | 0.1828 | 19.0 |
| 1.8221 | 3.0 | 1293 | 1.6040 | 0.2179 | 0.0942 | 0.1839 | 0.1839 | 19.0 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
google-t5/t5-small