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---
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: 0.6526104417670683
---
<!-- 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. -->
# 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: 0.8165
- Accuracy: 0.6526
## 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: 192
- eval_batch_size: 192
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0253 | 1.0 | 2046 | 0.8330 | 0.6382 |
| 0.9659 | 2.0 | 4092 | 0.8105 | 0.6530 |
| 0.9445 | 3.0 | 6138 | 0.7978 | 0.6558 |
| 0.9254 | 4.0 | 8184 | 0.7791 | 0.6594 |
| 0.9075 | 5.0 | 10230 | 0.7792 | 0.6614 |
| 0.8892 | 6.0 | 12276 | 0.7812 | 0.6554 |
| 0.8728 | 7.0 | 14322 | 0.7762 | 0.6538 |
| 0.8565 | 8.0 | 16368 | 0.8019 | 0.6494 |
| 0.8427 | 9.0 | 18414 | 0.8067 | 0.6558 |
| 0.8332 | 10.0 | 20460 | 0.8165 | 0.6526 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|