Instructions to use zenocode-org/t5-fra-cor-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenocode-org/t5-fra-cor-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("zenocode-org/t5-fra-cor-small") model = AutoModelForSeq2SeqLM.from_pretrained("zenocode-org/t5-fra-cor-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t5-fra-cor-small
This model is a fine-tuned version of google-t5/t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.1998
- Bleu: 1.4953
- Gen Len: 68.0984
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| 16.2996 | 1.0 | 290 | 3.4781 | 1.3942 | 76.5738 |
| 14.3797 | 2.0 | 580 | 3.2525 | 1.4423 | 68.4918 |
| 13.9806 | 3.0 | 870 | 3.1998 | 1.4953 | 68.0984 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
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Base model
google-t5/t5-small