---
library_name: transformers
base_model: UBC-NLP/MARBERTv2
tags:
- generated_from_trainer
model-index:
- name: MASRIBERTV4
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# MASRIBERTV4

This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/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