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--- |
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license: mit |
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langs: |
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- multilingual |
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tags: |
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- generated_from_trainer |
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- xnli |
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datasets: |
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- xglue |
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metrics: |
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- accuracy |
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model-index: |
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- name: xlm-v-base-finetuned-xglue-xnli |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: xglue |
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type: xglue |
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config: xnli |
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split: validation.en+validation.ar+validation.bg+validation.de+validation.el+validation.es+validation.fr+validation.hi+validation.ru+validation.sw+validation.th+validation.tr+validation.ur+validation.vi+validation.zh |
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args: xnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7402677376171352 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# XLM-V (base) fine-tuned on XNLI |
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This model is a fine-tuned version of [XLM-V (base)](https://huggingface.co/facebook/xlm-v-base) on the XNLI (XGLUE) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6511 |
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- Accuracy: 0.7403 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 1.0994 | 0.08 | 1000 | 1.0966 | 0.3697 | |
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| 1.0221 | 0.16 | 2000 | 1.0765 | 0.4560 | |
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| 0.8437 | 0.24 | 3000 | 0.8472 | 0.6179 | |
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| 0.6997 | 0.33 | 4000 | 0.7650 | 0.6804 | |
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| 0.6304 | 0.41 | 5000 | 0.7227 | 0.7007 | |
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| 0.5972 | 0.49 | 6000 | 0.7430 | 0.6977 | |
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| 0.5886 | 0.57 | 7000 | 0.7365 | 0.7066 | |
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| 0.5585 | 0.65 | 8000 | 0.6819 | 0.7223 | |
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| 0.5464 | 0.73 | 9000 | 0.7222 | 0.7046 | |
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| 0.5289 | 0.81 | 10000 | 0.7290 | 0.7054 | |
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| 0.5298 | 0.9 | 11000 | 0.6824 | 0.7221 | |
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| 0.5241 | 0.98 | 12000 | 0.6650 | 0.7268 | |
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| 0.4806 | 1.06 | 13000 | 0.6861 | 0.7308 | |
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| 0.4715 | 1.14 | 14000 | 0.6619 | 0.7304 | |
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| 0.4645 | 1.22 | 15000 | 0.6656 | 0.7284 | |
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| 0.4443 | 1.3 | 16000 | 0.7026 | 0.7270 | |
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| 0.4582 | 1.39 | 17000 | 0.7055 | 0.7225 | |
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| 0.4456 | 1.47 | 18000 | 0.6592 | 0.7361 | |
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| 0.44 | 1.55 | 19000 | 0.6816 | 0.7329 | |
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| 0.4419 | 1.63 | 20000 | 0.6772 | 0.7357 | |
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| 0.4403 | 1.71 | 21000 | 0.6745 | 0.7319 | |
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| 0.4348 | 1.79 | 22000 | 0.6678 | 0.7338 | |
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| 0.4355 | 1.87 | 23000 | 0.6614 | 0.7365 | |
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| 0.4295 | 1.96 | 24000 | 0.6511 | 0.7403 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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