--- license: mit tags: - text-classification - generated_from_trainer datasets: - xnli metrics: - accuracy model-index: - name: xnli_xlm_r_only_ur results: - task: name: Text Classification type: text-classification dataset: name: xnli type: xnli config: ur split: train args: ur metrics: - name: Accuracy type: accuracy value: 0.6514056224899598 --- # xnli_xlm_r_only_ur This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset. It achieves the following results on the evaluation set: - Loss: 0.8516 - Accuracy: 0.6514 ## 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: 1.5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.0129 | 1.0 | 3068 | 0.8285 | 0.6357 | | 0.9628 | 2.0 | 6136 | 0.8120 | 0.6470 | | 0.9407 | 3.0 | 9204 | 0.7934 | 0.6643 | | 0.9205 | 4.0 | 12272 | 0.7802 | 0.6546 | | 0.9001 | 5.0 | 15340 | 0.7820 | 0.6594 | | 0.8791 | 6.0 | 18408 | 0.8046 | 0.6502 | | 0.8593 | 7.0 | 21476 | 0.7950 | 0.6627 | | 0.8404 | 8.0 | 24544 | 0.8231 | 0.6514 | | 0.8242 | 9.0 | 27612 | 0.8376 | 0.6558 | | 0.8118 | 10.0 | 30680 | 0.8516 | 0.6514 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1