Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Mingmingchenxin/summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mingmingchenxin/summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mingmingchenxin/summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Mingmingchenxin/summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
summarization
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: 2.7096
- Rouge1: 0.1409
- Rouge2: 0.0444
- Rougel: 0.116
- Rougelsum: 0.1161
- Gen Len: 20.0
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use 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: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 31 | 3.1664 | 0.1398 | 0.0448 | 0.1151 | 0.115 | 20.0 |
| No log | 2.0 | 62 | 2.8497 | 0.1339 | 0.0411 | 0.1104 | 0.1106 | 20.0 |
| No log | 3.0 | 93 | 2.7372 | 0.1392 | 0.0442 | 0.1146 | 0.1146 | 20.0 |
| No log | 4.0 | 124 | 2.7096 | 0.1409 | 0.0444 | 0.116 | 0.1161 | 20.0 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Base model
google-t5/t5-small