Instructions to use drive087/wikinews_t5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use drive087/wikinews_t5-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("drive087/wikinews_t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("drive087/wikinews_t5-small") - Notebooks
- Google Colab
- Kaggle
wikinews_model
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0753
- Rouge1: 0.2105
- Rouge2: 0.1842
- Rougel: 0.2105
- Rougelsum: 0.2105
- Gen Len: 9.3158
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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.7014 | 35.71 | 500 | 0.6265 | 0.2675 | 0.1316 | 0.2675 | 0.2675 | 9.3684 |
| 0.3433 | 71.43 | 1000 | 0.7564 | 0.2807 | 0.1579 | 0.2807 | 0.2807 | 9.7895 |
| 0.2291 | 107.14 | 1500 | 0.9650 | 0.2632 | 0.1842 | 0.2632 | 0.2632 | 9.5 |
| 0.1626 | 142.86 | 2000 | 1.0753 | 0.2105 | 0.1842 | 0.2105 | 0.2105 | 9.3158 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Downloads last month
- 3
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support