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This model is a fine-tuned version of longformer-gottbert-base-8192-aw512- on the a 500 million token subset of the german parts of the OSCAR dataset. It achieves the following results on the custom evaluation set:
- Loss: 1.4981
Model description
The weights of the model are initialized from the german version of Roberta gottbert-base. The local attention windows have a fixed size of 512 tokens across all layers. The maximum sequence length is 8192.
Intended uses & limitations
Longformer models enable processing long texts using a mixture of local attention on each subword token and task specific global attention on a subset of the tokens.
Training and evaluation data
The OSCAR dataset is freely avaible corpus of filtered web texts from the Common Crawl in various languages. We used the 2017 version of the dataset.
Training procedure
The model was trained with masked language modeling for 3 epochs on a customly created 500 million tokens subset of the german proportion of the OSCAR dataset. It was validated using 5% of the original subset.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5636 | 0.1 | 500 | 2.2399 |
| 2.0426 | 0.2 | 1000 | 1.8841 |
| 1.9653 | 0.3 | 1500 | 1.7807 |
| 1.9422 | 0.4 | 2000 | 1.7206 |
| 1.9323 | 0.49 | 2500 | 1.6800 |
| 1.7587 | 0.59 | 3000 | 1.6507 |
| 1.7239 | 0.69 | 3500 | 1.6316 |
| 1.7452 | 0.79 | 4000 | 1.6137 |
| 1.7415 | 0.89 | 4500 | 1.5983 |
| 1.7733 | 0.99 | 5000 | 1.5830 |
| 1.7656 | 1.09 | 5500 | 1.5735 |
| 1.6543 | 1.19 | 6000 | 1.5643 |
| 1.7131 | 1.28 | 6500 | 1.5546 |
| 1.6456 | 1.38 | 7000 | 1.5503 |
| 1.716 | 1.48 | 7500 | 1.5422 |
| 1.806 | 1.58 | 8000 | 1.5377 |
| 1.8407 | 1.68 | 8500 | 1.5327 |
| 1.6371 | 1.78 | 9000 | 1.5278 |
| 1.6453 | 1.88 | 9500 | 1.5231 |
| 1.7754 | 1.98 | 10000 | 1.5214 |
| 1.7695 | 2.08 | 10500 | 1.5165 |
| 1.7109 | 2.17 | 11000 | 1.5138 |
| 1.6992 | 2.27 | 11500 | 1.5107 |
| 1.6707 | 2.37 | 12000 | 1.5097 |
| 1.6835 | 2.47 | 12500 | 1.5040 |
| 1.7171 | 2.57 | 13000 | 1.5041 |
| 1.7257 | 2.67 | 13500 | 1.4990 |
| 1.6287 | 2.77 | 14000 | 1.5017 |
| 1.7737 | 2.87 | 14500 | 1.4983 |
| 1.4002 | 2.96 | 15000 | 1.4992 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.17.0
- Tokenizers 0.10.3
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