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xnli_xlm_r_only_ur

Text Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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Dan Semin commited on
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update model card README.md

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  1. README.md +17 -17
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@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.41847389558232934
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0581
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- - Accuracy: 0.4185
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  ## Model description
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@@ -52,29 +52,29 @@ More information needed
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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: 128
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- - eval_batch_size: 128
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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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- - lr_scheduler_warmup_steps: 100
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  - num_epochs: 10
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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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- | 1.0996 | 1.0 | 3068 | 1.0988 | 0.3333 |
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- | 1.1 | 2.0 | 6136 | 1.0986 | 0.3333 |
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- | 1.0999 | 3.0 | 9204 | 1.0986 | 0.3333 |
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- | 1.0998 | 4.0 | 12272 | 1.0988 | 0.3333 |
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- | 1.0998 | 5.0 | 15340 | 1.0986 | 0.3333 |
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- | 1.0994 | 6.0 | 18408 | 1.0987 | 0.3333 |
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- | 1.0994 | 7.0 | 21476 | 1.0987 | 0.3333 |
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- | 1.0993 | 8.0 | 24544 | 1.0987 | 0.3333 |
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- | 1.0932 | 9.0 | 27612 | 1.0692 | 0.4177 |
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- | 1.075 | 10.0 | 30680 | 1.0581 | 0.4185 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6526104417670683
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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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  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8165
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+ - Accuracy: 0.6526
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 1.5e-05
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+ - train_batch_size: 192
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+ - eval_batch_size: 192
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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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+ - lr_scheduler_warmup_steps: 1000
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  - num_epochs: 10
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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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+ | 1.0253 | 1.0 | 2046 | 0.8330 | 0.6382 |
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+ | 0.9659 | 2.0 | 4092 | 0.8105 | 0.6530 |
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+ | 0.9445 | 3.0 | 6138 | 0.7978 | 0.6558 |
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+ | 0.9254 | 4.0 | 8184 | 0.7791 | 0.6594 |
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+ | 0.9075 | 5.0 | 10230 | 0.7792 | 0.6614 |
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+ | 0.8892 | 6.0 | 12276 | 0.7812 | 0.6554 |
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+ | 0.8728 | 7.0 | 14322 | 0.7762 | 0.6538 |
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+ | 0.8565 | 8.0 | 16368 | 0.8019 | 0.6494 |
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+ | 0.8427 | 9.0 | 18414 | 0.8067 | 0.6558 |
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+ | 0.8332 | 10.0 | 20460 | 0.8165 | 0.6526 |
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  ### Framework versions