# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("T0KII/MASRIBERTV4")
model = AutoModelForMaskedLM.from_pretrained("T0KII/MASRIBERTV4", device_map="auto")Quick Links
MASRIBERTV4
This model is a fine-tuned version of UBC-NLP/MARBERTv2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4905
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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10000
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 49.3326 | 0.0427 | 500 | 5.4990 |
| 42.0818 | 0.0854 | 1000 | 4.8453 |
| 38.9605 | 0.1281 | 1500 | 4.5192 |
| 36.9452 | 0.1709 | 2000 | 4.3034 |
| 35.4721 | 0.2136 | 2500 | 4.1469 |
| 17.1800 | 0.2563 | 3000 | 2.0153 |
| 16.6866 | 0.2990 | 3500 | 1.9642 |
| 16.3991 | 0.3417 | 4000 | 1.9193 |
| 16.0343 | 0.3844 | 4500 | 1.8890 |
| 15.7752 | 0.4271 | 5000 | 1.8541 |
| 15.5412 | 0.4699 | 5500 | 1.8296 |
| 15.3676 | 0.5126 | 6000 | 1.8037 |
| 15.1004 | 0.5553 | 6500 | 1.7819 |
| 14.9579 | 0.5980 | 7000 | 1.7591 |
| 14.8206 | 0.6407 | 7500 | 1.7475 |
| 14.5731 | 0.6834 | 8000 | 1.7312 |
| 14.4853 | 0.7262 | 8500 | 1.7171 |
| 14.4356 | 0.7689 | 9000 | 1.7090 |
| 14.2340 | 0.8116 | 9500 | 1.6929 |
| 14.2497 | 0.8543 | 10000 | 1.6809 |
| 14.1660 | 0.8970 | 10500 | 1.6745 |
| 14.0404 | 0.9397 | 11000 | 1.6584 |
| 13.8540 | 0.9824 | 11500 | 1.6474 |
| 13.7538 | 1.0251 | 12000 | 1.6362 |
| 13.6777 | 1.0678 | 12500 | 1.6223 |
| 13.5928 | 1.1105 | 13000 | 1.6111 |
| 13.4528 | 1.1533 | 13500 | 1.6016 |
| 13.3583 | 1.1960 | 14000 | 1.5926 |
| 13.3129 | 1.2387 | 14500 | 1.5797 |
| 13.2261 | 1.2814 | 15000 | 1.5714 |
| 13.2270 | 1.3241 | 15500 | 1.5613 |
| 13.0836 | 1.3668 | 16000 | 1.5562 |
| 13.0298 | 1.4096 | 16500 | 1.5463 |
| 12.9826 | 1.4523 | 17000 | 1.5360 |
| 12.9178 | 1.4950 | 17500 | 1.5267 |
| 12.8210 | 1.5377 | 18000 | 1.5218 |
| 12.7591 | 1.5804 | 18500 | 1.5172 |
| 12.7104 | 1.6231 | 19000 | 1.5090 |
| 12.6183 | 1.6658 | 19500 | 1.5056 |
| 12.5794 | 1.7086 | 20000 | 1.5013 |
| 12.5963 | 1.7513 | 20500 | 1.4976 |
| 12.5140 | 1.7940 | 21000 | 1.4922 |
| 12.4895 | 1.8367 | 21500 | 1.4932 |
| 12.4931 | 1.8794 | 22000 | 1.4914 |
| 12.5685 | 1.9221 | 22500 | 1.4908 |
| 12.4823 | 1.9648 | 23000 | 1.4929 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for T0KII/MASRIBERTV4
Base model
UBC-NLP/MARBERTv2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="T0KII/MASRIBERTV4")