# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("shed-e/Summary")
model = AutoModelForSeq2SeqLM.from_pretrained("shed-e/Summary")Quick Links
mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0318
- Rouge1: 0.1806
- Rouge2: 0.0917
- Rougel: 0.1765
- Rougelsum: 0.1766
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: 5.6e-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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 6.6121 | 1.0 | 1209 | 3.2969 | 0.1522 | 0.0635 | 0.1476 | 0.147 |
| 3.901 | 2.0 | 2418 | 3.1307 | 0.1672 | 0.0821 | 0.1604 | 0.1602 |
| 3.5788 | 3.0 | 3627 | 3.0910 | 0.1804 | 0.0922 | 0.1748 | 0.1751 |
| 3.4198 | 4.0 | 4836 | 3.0646 | 0.1717 | 0.0813 | 0.167 | 0.1664 |
| 3.321 | 5.0 | 6045 | 3.0659 | 0.1782 | 0.0877 | 0.1759 | 0.1756 |
| 3.2441 | 6.0 | 7254 | 3.0407 | 0.1785 | 0.088 | 0.1755 | 0.1751 |
| 3.2075 | 7.0 | 8463 | 3.0356 | 0.1789 | 0.09 | 0.1743 | 0.1747 |
| 3.1803 | 8.0 | 9672 | 3.0318 | 0.1806 | 0.0917 | 0.1765 | 0.1766 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="shed-e/Summary")