End of training
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README.md
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---
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license: mit
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base_model: xlm-roberta-
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tags:
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- generated_from_trainer
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model-index:
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- name: mtl-xlmr-
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results: []
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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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# mtl-xlmr-
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This model is a fine-tuned version of [xlm-roberta-
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size:
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- eval_batch_size:
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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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### Training results
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| Training Loss | Epoch | Step
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### Framework versions
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---
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license: mit
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base_model: xlm-roberta-large
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tags:
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- generated_from_trainer
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model-index:
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- name: mtl-xlmr-large-viwiki-v2
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results: []
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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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# mtl-xlmr-large-viwiki-v2
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This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6167
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 8
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- eval_batch_size: 4
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:-----:|:---------------:|
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| 0.5611 | 1.0 | 1920 | 0.5882 |
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| 0.3039 | 2.0 | 3840 | 0.5782 |
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| 0.2045 | 3.0 | 5760 | 0.5083 |
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| 0.2969 | 4.0 | 7680 | 0.7146 |
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| 0.0895 | 5.0 | 9600 | 0.8017 |
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| 0.0781 | 6.0 | 11520 | 1.0214 |
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| 0.0002 | 7.0 | 13440 | 1.1289 |
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| 0.0029 | 8.0 | 15360 | 1.4217 |
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| 0.041 | 9.0 | 17280 | 1.5223 |
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| 0.0 | 10.0 | 19200 | 1.6167 |
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### Framework versions
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model.safetensors
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