Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Ramu143/full_finetine_flan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ramu143/full_finetine_flan with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ramu143/full_finetine_flan") model = AutoModelForSeq2SeqLM.from_pretrained("Ramu143/full_finetine_flan") - Notebooks
- Google Colab
- Kaggle
full_finetine_flan
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0005
- Rouge2: 0.0
- Rougel: 0.0006
- Rougelsum: 0.0006
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 4213 | nan | 0.0005 | 0.0 | 0.0006 | 0.0006 |
| 0.0 | 2.0 | 8426 | nan | 0.0005 | 0.0 | 0.0006 | 0.0006 |
| 0.0 | 3.0 | 12639 | nan | 0.0005 | 0.0 | 0.0006 | 0.0006 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for Ramu143/full_finetine_flan
Base model
google/flan-t5-base