first
This model is a fine-tuned version of nystromformer-gottbert-base-8192 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5135
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: 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 |
---|---|---|---|
6.7133 | 0.1 | 500 | 6.6155 |
2.7876 | 0.2 | 1000 | 2.5542 |
2.1831 | 0.3 | 1500 | 2.0356 |
2.0316 | 0.4 | 2000 | 1.8793 |
2.0678 | 0.49 | 2500 | 1.7954 |
1.8182 | 0.59 | 3000 | 1.7473 |
1.7393 | 0.69 | 3500 | 1.7081 |
1.7586 | 0.79 | 4000 | 1.6787 |
1.7417 | 0.89 | 4500 | 1.6563 |
1.8256 | 0.99 | 5000 | 1.6370 |
1.7957 | 1.09 | 5500 | 1.6219 |
1.6876 | 1.19 | 6000 | 1.6084 |
1.7172 | 1.28 | 6500 | 1.5941 |
1.6564 | 1.38 | 7000 | 1.5881 |
1.732 | 1.48 | 7500 | 1.5757 |
1.8272 | 1.58 | 8000 | 1.5692 |
1.7951 | 1.68 | 8500 | 1.5617 |
1.6669 | 1.78 | 9000 | 1.5546 |
1.6489 | 1.88 | 9500 | 1.5458 |
1.772 | 1.98 | 10000 | 1.5439 |
1.7424 | 2.08 | 10500 | 1.5379 |
1.7077 | 2.17 | 11000 | 1.5322 |
1.6926 | 2.27 | 11500 | 1.5294 |
1.656 | 2.37 | 12000 | 1.5274 |
1.7002 | 2.47 | 12500 | 1.5201 |
1.7102 | 2.57 | 13000 | 1.5197 |
1.7158 | 2.67 | 13500 | 1.5162 |
1.6081 | 2.77 | 14000 | 1.5169 |
1.754 | 2.87 | 14500 | 1.5140 |
1.3588 | 2.96 | 15000 | 1.5135 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
- Downloads last month
- 7
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.