safetensors generated_from_trainer base_model:facebook/xlm-roberta-xl license:mit region:us
---
license: mit
base_model: facebook/xlm-roberta-xl
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-xl-final-lora500
  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-lora500

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: 1.5378
- Precision: 0.9334
- Recall: 0.9341
- F1: 0.9337
- Accuracy: 0.9421

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.6949        | 1.0   | 250   | 1.9846          | 0.7571    | 0.8198 | 0.7872 | 0.8265   |
| 1.8141        | 2.0   | 500   | 1.6856          | 0.8709    | 0.8824 | 0.8766 | 0.8938   |
| 1.6277        | 3.0   | 750   | 1.6081          | 0.8881    | 0.9011 | 0.8945 | 0.9122   |
| 1.5464        | 4.0   | 1000  | 1.5735          | 0.9004    | 0.9064 | 0.9034 | 0.9201   |
| 1.4908        | 5.0   | 1250  | 1.5482          | 0.9111    | 0.9145 | 0.9128 | 0.9274   |
| 1.4599        | 6.0   | 1500  | 1.5386          | 0.9096    | 0.9175 | 0.9135 | 0.9282   |
| 1.4382        | 7.0   | 1750  | 1.5396          | 0.9175    | 0.9204 | 0.9189 | 0.9292   |
| 1.422         | 8.0   | 2000  | 1.5394          | 0.9163    | 0.9212 | 0.9188 | 0.9305   |
| 1.4053        | 9.0   | 2250  | 1.5354          | 0.9240    | 0.9223 | 0.9231 | 0.9335   |
| 1.3949        | 10.0  | 2500  | 1.5424          | 0.9155    | 0.9230 | 0.9192 | 0.9308   |
| 1.3858        | 11.0  | 2750  | 1.5405          | 0.9202    | 0.9248 | 0.9225 | 0.9313   |
| 1.379         | 12.0  | 3000  | 1.5364          | 0.9186    | 0.9263 | 0.9224 | 0.9339   |
| 1.3715        | 13.0  | 3250  | 1.5310          | 0.9263    | 0.9275 | 0.9269 | 0.9373   |
| 1.3647        | 14.0  | 3500  | 1.5321          | 0.9221    | 0.9273 | 0.9247 | 0.9355   |
| 1.3592        | 15.0  | 3750  | 1.5347          | 0.9277    | 0.9261 | 0.9269 | 0.9372   |
| 1.3564        | 16.0  | 4000  | 1.5323          | 0.9229    | 0.9269 | 0.9249 | 0.9371   |
| 1.3524        | 17.0  | 4250  | 1.5339          | 0.9232    | 0.9248 | 0.9240 | 0.9347   |
| 1.3512        | 18.0  | 4500  | 1.5425          | 0.9262    | 0.9284 | 0.9273 | 0.9370   |
| 1.3482        | 19.0  | 4750  | 1.5387          | 0.9238    | 0.9299 | 0.9268 | 0.9362   |
| 1.3437        | 20.0  | 5000  | 1.5334          | 0.9267    | 0.9324 | 0.9295 | 0.9389   |
| 1.3414        | 21.0  | 5250  | 1.5379          | 0.9302    | 0.9283 | 0.9292 | 0.9394   |
| 1.3408        | 22.0  | 5500  | 1.5394          | 0.9256    | 0.9291 | 0.9273 | 0.9381   |
| 1.3401        | 23.0  | 5750  | 1.5376          | 0.9320    | 0.9301 | 0.9310 | 0.9391   |
| 1.3388        | 24.0  | 6000  | 1.5381          | 0.9300    | 0.9300 | 0.9300 | 0.9383   |
| 1.3379        | 25.0  | 6250  | 1.5402          | 0.9247    | 0.9309 | 0.9278 | 0.9380   |
| 1.3361        | 26.0  | 6500  | 1.5415          | 0.9303    | 0.9275 | 0.9289 | 0.9383   |
| 1.3349        | 27.0  | 6750  | 1.5391          | 0.9305    | 0.9300 | 0.9302 | 0.9402   |
| 1.3338        | 28.0  | 7000  | 1.5379          | 0.9296    | 0.9290 | 0.9293 | 0.9392   |
| 1.3337        | 29.0  | 7250  | 1.5438          | 0.9286    | 0.9309 | 0.9297 | 0.9388   |
| 1.3329        | 30.0  | 7500  | 1.5388          | 0.9325    | 0.9310 | 0.9318 | 0.9410   |
| 1.3321        | 31.0  | 7750  | 1.5443          | 0.9319    | 0.9314 | 0.9317 | 0.9408   |
| 1.3319        | 32.0  | 8000  | 1.5413          | 0.9317    | 0.9334 | 0.9325 | 0.9415   |
| 1.3313        | 33.0  | 8250  | 1.5428          | 0.9329    | 0.9332 | 0.9331 | 0.9413   |
| 1.3309        | 34.0  | 8500  | 1.5452          | 0.9288    | 0.9317 | 0.9302 | 0.9396   |
| 1.3308        | 35.0  | 8750  | 1.5382          | 0.9307    | 0.9324 | 0.9315 | 0.9410   |
| 1.3307        | 36.0  | 9000  | 1.5370          | 0.9314    | 0.9334 | 0.9324 | 0.9413   |
| 1.33          | 37.0  | 9250  | 1.5391          | 0.9321    | 0.9328 | 0.9325 | 0.9414   |
| 1.3297        | 38.0  | 9500  | 1.5386          | 0.9330    | 0.9335 | 0.9333 | 0.9414   |
| 1.3293        | 39.0  | 9750  | 1.5378          | 0.9336    | 0.9343 | 0.9340 | 0.9420   |
| 1.3294        | 40.0  | 10000 | 1.5378          | 0.9334    | 0.9341 | 0.9337 | 0.9421   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
