# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Dauka-transformers/interpro_bert")
model = AutoModelForMaskedLM.from_pretrained("Dauka-transformers/interpro_bert")Quick Links
interpro_bert
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7610
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: 2e-05
- train_batch_size: 256
- eval_batch_size: 128
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 2048
- total_eval_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.7929 | 1.0 | 14395 | 4.4007 |
| 2.6085 | 2.0 | 28790 | 2.4082 |
| 1.7765 | 3.0 | 43185 | 1.6582 |
| 1.3909 | 4.0 | 57580 | 1.3030 |
| 1.1805 | 5.0 | 71975 | 1.1146 |
| 1.0593 | 6.0 | 86370 | 1.0107 |
| 0.9726 | 7.0 | 100765 | 0.9359 |
| 0.9167 | 8.0 | 115160 | 0.8880 |
| 0.8683 | 9.0 | 129555 | 0.8475 |
| 0.8436 | 10.0 | 143950 | 0.8224 |
| 0.8167 | 11.0 | 158345 | 0.7974 |
| 0.8009 | 12.0 | 172740 | 0.7850 |
| 0.7874 | 13.0 | 187135 | 0.7682 |
| 0.7772 | 14.0 | 201530 | 0.7654 |
| 0.7738 | 15.0 | 215925 | 0.7610 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Dauka-transformers/interpro_bert")