Summarization
Transformers
PyTorch
German
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Einmalumdiewelt/T5-Base_GNAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Einmalumdiewelt/T5-Base_GNAD 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="Einmalumdiewelt/T5-Base_GNAD")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Einmalumdiewelt/T5-Base_GNAD") model = AutoModelForSeq2SeqLM.from_pretrained("Einmalumdiewelt/T5-Base_GNAD") - Notebooks
- Google Colab
- Kaggle
T5-Base_GNAD
This model is a fine-tuned version of Einmalumdiewelt/T5-Base_GNAD on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.1025
- Rouge1: 27.5357
- Rouge2: 8.5623
- Rougel: 19.1508
- Rougelsum: 23.9029
- Gen Len: 52.7253
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: 5e-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: 10.0
Training results
Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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