Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use baovox/fine-tuning-vit5-mlgsum-relu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baovox/fine-tuning-vit5-mlgsum-relu with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("baovox/fine-tuning-vit5-mlgsum-relu") model = AutoModelForSeq2SeqLM.from_pretrained("baovox/fine-tuning-vit5-mlgsum-relu", device_map="auto") - Notebooks
- Google Colab
- Kaggle
fine-tuning-vit5-mlgsum-relu
This model is a fine-tuned version of VietAI/vit5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.1553
- Rouge1: 50.2842
- Rouge2: 21.0293
- Rougel: 33.3278
- Rougelsum: 33.59
- Gen Len: 22.7732
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.2569 | 1.0 | 5972 | 2.1921 | 50.1697 | 20.3516 | 33.0218 | 33.2741 | 22.798 |
| 2.1195 | 2.0 | 11944 | 2.1568 | 49.9364 | 20.7226 | 33.2213 | 33.4414 | 22.7167 |
| 1.9927 | 3.0 | 17916 | 2.1553 | 50.2842 | 21.0293 | 33.3278 | 33.59 | 22.7732 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Base model
VietAI/vit5-base