Instructions to use Prience91/mt5_book_review_summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prience91/mt5_book_review_summary with Transformers:
# 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="Prience91/mt5_book_review_summary")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Prience91/mt5_book_review_summary") model = AutoModelForSeq2SeqLM.from_pretrained("Prience91/mt5_book_review_summary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
mt5_book_review_summary
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: 3.0376
- Rouge1: 0.1194
- Rouge2: 0.0455
- Rougel: 0.1178
- Rougelsum: 0.1177
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 2.8317 | 1.0 | 2202 | 3.2768 | 0.1309 | 0.0480 | 0.1290 | 0.1292 |
| 3.4955 | 2.0 | 4404 | 3.0428 | 0.1220 | 0.0465 | 0.1204 | 0.1204 |
| 3.3529 | 3.0 | 6606 | 3.0376 | 0.1194 | 0.0455 | 0.1178 | 0.1177 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Prience91/mt5_book_review_summary
Base model
google/mt5-small