Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Patcas/summary_t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Patcas/summary_t5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Patcas/summary_t5") model = AutoModelForSeq2SeqLM.from_pretrained("Patcas/summary_t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Patcas/summary_t5: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/Patcas/summary_t5/resolve/main/README.md
- Command line
-
hf download hf://Patcas/summary_t5/README.md
-
curl -L -o README.md https://huggingface.co/Patcas/summary_t5/resolve/main/README.md
1.47 kB
metadata
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
model-index:
- name: summary_t5
results: []
summary_t5
This model is a fine-tuned version of google-t5/t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8788
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 278 | 0.9567 |
| 1.1381 | 2.0 | 556 | 0.9041 |
| 1.1381 | 3.0 | 834 | 0.8909 |
| 0.8656 | 4.0 | 1112 | 0.8797 |
| 0.8656 | 5.0 | 1390 | 0.8788 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2