Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use Redhammer000/flan-t5-base-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Redhammer000/flan-t5-base-samsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Redhammer000/flan-t5-base-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("Redhammer000/flan-t5-base-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flan-t5-base-samsum
This model is a fine-tuned version of google/flan-t5-base on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.3909
- Rouge1: 46.4923
- Rouge2: 22.91
- Rougel: 39.07
- Rougelsum: 42.9091
- Gen Len: 17.2711
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.4189 | 1.0 | 1842 | 1.3909 | 46.4923 | 22.91 | 39.07 | 42.9091 | 17.2711 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for Redhammer000/flan-t5-base-samsum
Base model
google/flan-t5-baseEvaluation results
- Rouge1 on samsumtest set self-reported46.492