Instructions to use shkna1368/umt5-small-finetuned-umt5-poemV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shkna1368/umt5-small-finetuned-umt5-poemV1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("shkna1368/umt5-small-finetuned-umt5-poemV1") model = AutoModelForSeq2SeqLM.from_pretrained("shkna1368/umt5-small-finetuned-umt5-poemV1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
umt5-small-finetuned-umt5-poemV1
This model is a fine-tuned version of google/umt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 282 | nan |
| 0.0 | 2.0 | 564 | nan |
| 0.0 | 3.0 | 846 | nan |
| 0.0 | 4.0 | 1128 | nan |
| 0.0 | 5.0 | 1410 | nan |
| 0.0 | 6.0 | 1692 | nan |
| 0.0 | 7.0 | 1974 | nan |
| 0.0 | 8.0 | 2256 | nan |
| 0.0 | 9.0 | 2538 | nan |
| 0.0 | 10.0 | 2820 | nan |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for shkna1368/umt5-small-finetuned-umt5-poemV1
Base model
google/umt5-small