Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use jgibb/t-5_small_test_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jgibb/t-5_small_test_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jgibb/t-5_small_test_3") model = AutoModelForSeq2SeqLM.from_pretrained("jgibb/t-5_small_test_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t-5_small_test_3
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5645
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.09 | 250 | 1.7997 |
| 2.4572 | 0.18 | 500 | 1.6985 |
| 2.4572 | 0.27 | 750 | 1.6370 |
| 1.7999 | 0.35 | 1000 | 1.6171 |
| 1.7999 | 0.44 | 1250 | 1.5987 |
| 1.7654 | 0.53 | 1500 | 1.5850 |
| 1.7654 | 0.62 | 1750 | 1.5795 |
| 1.6833 | 0.71 | 2000 | 1.5732 |
| 1.6833 | 0.8 | 2250 | 1.5690 |
| 1.6961 | 0.89 | 2500 | 1.5659 |
| 1.6961 | 0.98 | 2750 | 1.5645 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
google-t5/t5-small