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---
language:
- en
- ar
- bg
- de
- el
- fr
- hi
- ru
- es
- sw
- th
- tr
- ur
- vi
- zh
tags:
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: pixel-base-finetuned-xnli-translate-train-all
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: XNLI
type: xnli
args: xnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6254886211512718
---
<!-- 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. -->
# pixel-base-finetuned-xnli-translate-train-all
This model is a fine-tuned version of [Team-PIXEL/pixel-base](https://huggingface.co/Team-PIXEL/pixel-base) on the XNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8312
- Accuracy: 0.6255
## 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: 256
- eval_batch_size: 8
- seed: 555
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 50000
- mixed_precision_training: Apex, opt level O1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0422 | 0.04 | 1000 | 1.0647 | 0.4250 |
| 0.9622 | 0.09 | 2000 | 1.0015 | 0.5051 |
| 0.93 | 0.13 | 3000 | 0.9750 | 0.5285 |
| 0.9126 | 0.17 | 4000 | 0.9396 | 0.5488 |
| 0.9033 | 0.22 | 5000 | 0.9353 | 0.5603 |
| 0.8861 | 0.26 | 6000 | 0.9369 | 0.5606 |
| 0.8799 | 0.3 | 7000 | 0.9407 | 0.5575 |
| 0.8627 | 0.35 | 8000 | 0.9079 | 0.5774 |
| 0.8658 | 0.39 | 9000 | 0.9110 | 0.5711 |
| 0.8521 | 0.43 | 10000 | 0.8945 | 0.5837 |
| 0.8562 | 0.48 | 11000 | 0.8818 | 0.5871 |
| 0.8479 | 0.52 | 12000 | 0.8771 | 0.5938 |
| 0.8451 | 0.56 | 13000 | 0.8965 | 0.5844 |
| 0.8433 | 0.61 | 14000 | 0.8814 | 0.5937 |
| 0.8331 | 0.65 | 15000 | 0.8721 | 0.5983 |
| 0.8267 | 0.7 | 16000 | 0.8691 | 0.5978 |
| 0.8254 | 0.74 | 17000 | 0.8646 | 0.5999 |
| 0.8214 | 0.78 | 18000 | 0.8700 | 0.6004 |
| 0.815 | 0.83 | 19000 | 0.8621 | 0.6016 |
| 0.8145 | 0.87 | 20000 | 0.8482 | 0.6119 |
| 0.8067 | 0.91 | 21000 | 0.8601 | 0.6053 |
| 0.8063 | 0.96 | 22000 | 0.8535 | 0.6093 |
| 0.8008 | 1.0 | 23000 | 0.8455 | 0.6123 |
| 0.7863 | 1.04 | 24000 | 0.8524 | 0.6107 |
| 0.7918 | 1.09 | 25000 | 0.8450 | 0.6142 |
| 0.7746 | 1.13 | 26000 | 0.8531 | 0.6095 |
| 0.7855 | 1.17 | 27000 | 0.8442 | 0.6150 |
| 0.7903 | 1.22 | 28000 | 0.8386 | 0.6162 |
| 0.7808 | 1.26 | 29000 | 0.8403 | 0.6178 |
| 0.7847 | 1.3 | 30000 | 0.8421 | 0.6145 |
| 0.7822 | 1.35 | 31000 | 0.8427 | 0.6157 |
| 0.769 | 1.39 | 32000 | 0.8397 | 0.6187 |
| 0.7822 | 1.43 | 33000 | 0.8315 | 0.6213 |
| 0.771 | 1.48 | 34000 | 0.8505 | 0.6141 |
| 0.7713 | 1.52 | 35000 | 0.8482 | 0.6142 |
| 0.7663 | 1.56 | 36000 | 0.8490 | 0.6169 |
| 0.7653 | 1.61 | 37000 | 0.8295 | 0.6229 |
| 0.7669 | 1.65 | 38000 | 0.8313 | 0.6217 |
| 0.77 | 1.69 | 39000 | 0.8309 | 0.6234 |
| 0.763 | 1.74 | 40000 | 0.8310 | 0.6256 |
| 0.7609 | 1.78 | 41000 | 0.8302 | 0.6228 |
| 0.7627 | 1.83 | 42000 | 0.8242 | 0.6269 |
| 0.7617 | 1.87 | 43000 | 0.8232 | 0.6264 |
| 0.7636 | 1.91 | 44000 | 0.8265 | 0.6261 |
| 0.7585 | 1.96 | 45000 | 0.8258 | 0.6268 |
| 0.7572 | 2.0 | 46000 | 0.8223 | 0.6278 |
| 0.7396 | 2.04 | 47000 | 0.8348 | 0.6242 |
| 0.7344 | 2.09 | 48000 | 0.8299 | 0.6270 |
| 0.7385 | 2.13 | 49000 | 0.8314 | 0.6240 |
| 0.7275 | 2.17 | 50000 | 0.8312 | 0.6255 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.12.1
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