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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: roberta-finetuned-WebClassification-v2-smalllinguaMultiv2
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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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# roberta-finetuned-WebClassification-v2-smalllinguaMultiv2
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0950
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- Accuracy: 0.7742
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- F1: 0.7742
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- Precision: 0.7742
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- Recall: 0.7742
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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: 8
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- eval_batch_size: 8
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| No log | 1.0 | 48 | 2.8245 | 0.2796 | 0.2796 | 0.2796 | 0.2796 |
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| No log | 2.0 | 96 | 2.2338 | 0.4301 | 0.4301 | 0.4301 | 0.4301 |
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| No log | 3.0 | 144 | 1.9060 | 0.5269 | 0.5269 | 0.5269 | 0.5269 |
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| No log | 4.0 | 192 | 1.5349 | 0.6022 | 0.6022 | 0.6022 | 0.6022 |
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| No log | 5.0 | 240 | 1.4208 | 0.6882 | 0.6882 | 0.6882 | 0.6882 |
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| No log | 6.0 | 288 | 1.3330 | 0.7204 | 0.7204 | 0.7204 | 0.7204 |
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| No log | 7.0 | 336 | 1.2037 | 0.7097 | 0.7097 | 0.7097 | 0.7097 |
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| No log | 8.0 | 384 | 1.1414 | 0.7419 | 0.7419 | 0.7419 | 0.7419 |
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| No log | 9.0 | 432 | 1.0950 | 0.7742 | 0.7742 | 0.7742 | 0.7742 |
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| No log | 10.0 | 480 | 1.0883 | 0.7634 | 0.7634 | 0.7634 | 0.7634 |
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### Framework versions
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- Transformers 4.31.0.dev0
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- Pytorch 2.0.0
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- Datasets 2.1.0
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- Tokenizers 0.13.3
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