Summarization
Transformers
PyTorch
mt5
text2text-generation
Generated from Trainer
Eval Results (legacy)
Instructions to use lljllll2219/mt5-base-xlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lljllll2219/mt5-base-xlsum 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="lljllll2219/mt5-base-xlsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lljllll2219/mt5-base-xlsum") model = AutoModelForSeq2SeqLM.from_pretrained("lljllll2219/mt5-base-xlsum") - Notebooks
- Google Colab
- Kaggle
mt5-base-xlsum
This model is a fine-tuned version of google/mt5-base on the xlsum dataset. It achieves the following results on the evaluation set:
- Loss: 2.0396
- Rouge1: 2.98
- Rouge2: 0.1333
- Rougel: 3.0267
- Rougelsum: 2.9933
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 5.3745 | 1.0 | 500 | 2.5041 | 1.0696 | 0.13 | 1.062 | 1.0629 |
| 3.413 | 2.0 | 1000 | 2.2178 | 1.8333 | 0.1333 | 1.84 | 1.8633 |
| 3.1052 | 3.0 | 1500 | 2.0844 | 3.14 | 0.2667 | 3.18 | 3.1733 |
| 2.9673 | 4.0 | 2000 | 2.0396 | 2.98 | 0.1333 | 3.0267 | 2.9933 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for lljllll2219/mt5-base-xlsum
Base model
google/mt5-baseEvaluation results
- Rouge1 on xlsumself-reported2.980