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---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: XMLRoberta_70KURL
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. -->
# XMLRoberta_70KURL
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4150
- Accuracy: 0.9408
- F1: 0.9448
## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2150
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| No log | 0.4651 | 200 | 0.4701 | 0.8955 | 0.8673 |
| No log | 0.9302 | 400 | 0.1893 | 0.9359 | 0.9368 |
| No log | 1.3953 | 600 | 0.2241 | 0.9128 | 0.9192 |
| No log | 1.8605 | 800 | 0.2777 | 0.8848 | 0.8984 |
| 0.382 | 2.3256 | 1000 | 0.1388 | 0.9504 | 0.9525 |
| 0.382 | 2.7907 | 1200 | 0.1028 | 0.9694 | 0.9701 |
| 0.382 | 3.2558 | 1400 | 0.1413 | 0.9557 | 0.9579 |
| 0.382 | 3.7209 | 1600 | 0.0929 | 0.9718 | 0.9722 |
| 0.1521 | 4.1860 | 1800 | 0.1008 | 0.9695 | 0.9702 |
| 0.1521 | 4.6512 | 2000 | 0.1939 | 0.9238 | 0.9306 |
| 0.1521 | 5.1163 | 2200 | 0.1550 | 0.9401 | 0.9443 |
| 0.1521 | 5.5814 | 2400 | 0.0813 | 0.9744 | 0.9750 |
| 0.1044 | 6.0465 | 2600 | 0.2088 | 0.9193 | 0.9267 |
| 0.1044 | 6.5116 | 2800 | 0.1343 | 0.9523 | 0.9548 |
| 0.1044 | 6.9767 | 3000 | 0.2172 | 0.9219 | 0.9289 |
| 0.1044 | 7.4419 | 3200 | 0.1097 | 0.9656 | 0.9668 |
| 0.1044 | 7.9070 | 3400 | 0.3044 | 0.9147 | 0.9230 |
| 0.0762 | 8.3721 | 3600 | 0.2122 | 0.9283 | 0.9341 |
| 0.0762 | 8.8372 | 3800 | 0.1430 | 0.9532 | 0.9556 |
| 0.0762 | 9.3023 | 4000 | 0.2332 | 0.9312 | 0.9368 |
| 0.0762 | 9.7674 | 4200 | 0.2167 | 0.9297 | 0.9353 |
| 0.0564 | 10.2326 | 4400 | 0.1904 | 0.9475 | 0.9506 |
| 0.0564 | 10.6977 | 4600 | 0.2916 | 0.9196 | 0.9270 |
| 0.0564 | 11.1628 | 4800 | 0.2317 | 0.9451 | 0.9484 |
| 0.0564 | 11.6279 | 5000 | 0.2430 | 0.9475 | 0.9503 |
| 0.042 | 12.0930 | 5200 | 0.4035 | 0.9248 | 0.9315 |
| 0.042 | 12.5581 | 5400 | 0.3060 | 0.9352 | 0.9398 |
| 0.042 | 13.0233 | 5600 | 0.2894 | 0.9359 | 0.9407 |
| 0.042 | 13.4884 | 5800 | 0.2804 | 0.9439 | 0.9474 |
| 0.042 | 13.9535 | 6000 | 0.2941 | 0.9456 | 0.9490 |
| 0.0316 | 14.4186 | 6200 | 0.3211 | 0.9424 | 0.9460 |
| 0.0316 | 14.8837 | 6400 | 0.3453 | 0.9371 | 0.9416 |
| 0.0316 | 15.3488 | 6600 | 0.2587 | 0.9548 | 0.9569 |
| 0.0316 | 15.8140 | 6800 | 0.3433 | 0.9432 | 0.9468 |
| 0.025 | 16.2791 | 7000 | 0.3454 | 0.9416 | 0.9455 |
| 0.025 | 16.7442 | 7200 | 0.2977 | 0.9450 | 0.9484 |
| 0.025 | 17.2093 | 7400 | 0.3622 | 0.9452 | 0.9486 |
| 0.025 | 17.6744 | 7600 | 0.3035 | 0.9550 | 0.9572 |
| 0.0196 | 18.1395 | 7800 | 0.3588 | 0.9464 | 0.9496 |
| 0.0196 | 18.6047 | 8000 | 0.3714 | 0.9467 | 0.9500 |
| 0.0196 | 19.0698 | 8200 | 0.4517 | 0.9341 | 0.9391 |
| 0.0196 | 19.5349 | 8400 | 0.4078 | 0.9411 | 0.9451 |
| 0.0148 | 20.0 | 8600 | 0.4150 | 0.9408 | 0.9448 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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