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---
license: mit
base_model: facebook/xlm-roberta-xl
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-xl-final-lora152520
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. -->
# xlm-roberta-xl-final-lora152520
This model is a fine-tuned version of [facebook/xlm-roberta-xl](https://huggingface.co/facebook/xlm-roberta-xl) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5503
- Precision: 0.9267
- Recall: 0.9291
- F1: 0.9279
- Accuracy: 0.9386
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- num_epochs: 40
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.6885 | 1.0 | 250 | 1.9699 | 0.7944 | 0.8343 | 0.8139 | 0.8442 |
| 1.7948 | 2.0 | 500 | 1.6854 | 0.8702 | 0.8791 | 0.8746 | 0.8949 |
| 1.6148 | 3.0 | 750 | 1.6185 | 0.8827 | 0.8998 | 0.8911 | 0.9096 |
| 1.5365 | 4.0 | 1000 | 1.5710 | 0.9031 | 0.9054 | 0.9043 | 0.9195 |
| 1.4852 | 5.0 | 1250 | 1.5524 | 0.9124 | 0.9129 | 0.9126 | 0.9255 |
| 1.4538 | 6.0 | 1500 | 1.5431 | 0.9112 | 0.9176 | 0.9144 | 0.9272 |
| 1.4306 | 7.0 | 1750 | 1.5390 | 0.9145 | 0.9221 | 0.9183 | 0.9297 |
| 1.4132 | 8.0 | 2000 | 1.5358 | 0.9191 | 0.9219 | 0.9205 | 0.9321 |
| 1.4004 | 9.0 | 2250 | 1.5365 | 0.9174 | 0.9262 | 0.9218 | 0.9337 |
| 1.3883 | 10.0 | 2500 | 1.5407 | 0.9176 | 0.9263 | 0.9220 | 0.9332 |
| 1.3803 | 11.0 | 2750 | 1.5326 | 0.9218 | 0.9278 | 0.9248 | 0.9358 |
| 1.3727 | 12.0 | 3000 | 1.5353 | 0.9187 | 0.9245 | 0.9216 | 0.9329 |
| 1.3674 | 13.0 | 3250 | 1.5392 | 0.9202 | 0.9254 | 0.9228 | 0.9350 |
| 1.3609 | 14.0 | 3500 | 1.5384 | 0.9220 | 0.9259 | 0.9239 | 0.9347 |
| 1.3572 | 15.0 | 3750 | 1.5382 | 0.9201 | 0.9240 | 0.9220 | 0.9334 |
| 1.3522 | 16.0 | 4000 | 1.5410 | 0.9197 | 0.9270 | 0.9233 | 0.9342 |
| 1.3502 | 17.0 | 4250 | 1.5449 | 0.9245 | 0.9268 | 0.9256 | 0.9355 |
| 1.3456 | 18.0 | 4500 | 1.5439 | 0.9233 | 0.9278 | 0.9256 | 0.9360 |
| 1.3423 | 19.0 | 4750 | 1.5435 | 0.9259 | 0.9248 | 0.9253 | 0.9346 |
| 1.3419 | 20.0 | 5000 | 1.5432 | 0.9270 | 0.9282 | 0.9276 | 0.9371 |
| 1.3397 | 21.0 | 5250 | 1.5398 | 0.9250 | 0.9284 | 0.9267 | 0.9369 |
| 1.3377 | 22.0 | 5500 | 1.5411 | 0.9253 | 0.9270 | 0.9262 | 0.9358 |
| 1.3351 | 23.0 | 5750 | 1.5471 | 0.9274 | 0.9284 | 0.9279 | 0.9374 |
| 1.3369 | 24.0 | 6000 | 1.5542 | 0.9214 | 0.9240 | 0.9227 | 0.9339 |
| 1.3348 | 25.0 | 6250 | 1.5479 | 0.9268 | 0.9288 | 0.9278 | 0.9374 |
| 1.334 | 26.0 | 6500 | 1.5492 | 0.9268 | 0.9294 | 0.9281 | 0.9384 |
| 1.3334 | 27.0 | 6750 | 1.5471 | 0.9299 | 0.9287 | 0.9293 | 0.9377 |
| 1.3327 | 28.0 | 7000 | 1.5438 | 0.9291 | 0.9309 | 0.9300 | 0.9394 |
| 1.3314 | 29.0 | 7250 | 1.5445 | 0.9304 | 0.9315 | 0.9310 | 0.9403 |
| 1.3318 | 30.0 | 7500 | 1.5456 | 0.9291 | 0.9310 | 0.9301 | 0.9399 |
| 1.3312 | 31.0 | 7750 | 1.5474 | 0.9278 | 0.9295 | 0.9287 | 0.9386 |
| 1.3304 | 32.0 | 8000 | 1.5489 | 0.9273 | 0.9302 | 0.9288 | 0.9388 |
| 1.3298 | 33.0 | 8250 | 1.5469 | 0.9286 | 0.9299 | 0.9293 | 0.9388 |
| 1.3295 | 34.0 | 8500 | 1.5474 | 0.9291 | 0.9312 | 0.9302 | 0.9398 |
| 1.3288 | 35.0 | 8750 | 1.5518 | 0.9280 | 0.9300 | 0.9290 | 0.9386 |
| 1.3292 | 36.0 | 9000 | 1.5484 | 0.9271 | 0.9308 | 0.9289 | 0.9388 |
| 1.3287 | 37.0 | 9250 | 1.5487 | 0.9278 | 0.9297 | 0.9287 | 0.9382 |
| 1.328 | 38.0 | 9500 | 1.5492 | 0.9290 | 0.9305 | 0.9298 | 0.9394 |
| 1.3281 | 39.0 | 9750 | 1.5496 | 0.9278 | 0.9293 | 0.9285 | 0.9387 |
| 1.3285 | 40.0 | 10000 | 1.5503 | 0.9267 | 0.9291 | 0.9279 | 0.9386 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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