Instructions to use SouthMemphis/t5-tiny_for_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SouthMemphis/t5-tiny_for_summarization with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SouthMemphis/t5-tiny_for_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("SouthMemphis/t5-tiny_for_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SouthMemphis/t5-tiny_for_summarization: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
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https://huggingface.co/SouthMemphis/t5-tiny_for_summarization/resolve/main/README.md
- Command line
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hf download hf://SouthMemphis/t5-tiny_for_summarization/README.md
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curl -L -o README.md https://huggingface.co/SouthMemphis/t5-tiny_for_summarization/resolve/main/README.md
2.23 kB
metadata
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
- generated_from_keras_callback
model-index:
- name: SouthMemphis/t5-tiny_for_summarization
results: []
SouthMemphis/t5-tiny_for_summarization
This model is a fine-tuned version of google/t5-efficient-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 4.4877
- Validation Loss: 3.4974
- Train Rouge1: 21.2490
- Train Rouge2: 4.2075
- Train Rougel: 16.9993
- Train Rougelsum: 16.9842
- Train Gen Len: 16.678
- Epoch: 4
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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|---|---|---|---|---|---|---|---|
| 6.2678 | 3.9891 | 17.7562 | 3.0062 | 14.6825 | 14.7004 | 14.717 | 0 |
| 5.1618 | 3.7340 | 19.5200 | 3.7088 | 16.1766 | 16.1763 | 15.528 | 1 |
| 4.8511 | 3.6247 | 20.1377 | 3.7645 | 16.1460 | 16.1223 | 15.966 | 2 |
| 4.6068 | 3.5431 | 20.4053 | 3.9626 | 16.3460 | 16.3444 | 15.884 | 3 |
| 4.4877 | 3.4974 | 21.2490 | 4.2075 | 16.9993 | 16.9842 | 16.678 | 4 |
Framework versions
- Transformers 4.33.1
- TensorFlow 2.15.0-dev20230905
- Datasets 2.14.4
- Tokenizers 0.13.3