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fine_tuned_rte_sloberta

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README.md CHANGED
@@ -1,6 +1,6 @@
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  ---
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- license: mit
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- base_model: xlm-roberta-base
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # fine_tuned_rte_sloberta
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- This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4763
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  - Accuracy: 0.6207
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- - F1: 0.5951
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  ## Model description
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@@ -51,14 +51,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
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- | 0.7117 | 1.7241 | 50 | 0.7129 | 0.4138 | 0.2422 |
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- | 0.7033 | 3.4483 | 100 | 0.6997 | 0.4138 | 0.2422 |
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- | 0.6845 | 5.1724 | 150 | 0.6933 | 0.4828 | 0.4828 |
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- | 0.6378 | 6.8966 | 200 | 0.8005 | 0.4828 | 0.4668 |
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- | 0.4579 | 8.6207 | 250 | 0.9656 | 0.6207 | 0.5951 |
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- | 0.2521 | 10.3448 | 300 | 1.2302 | 0.6552 | 0.6018 |
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- | 0.1196 | 12.0690 | 350 | 1.4679 | 0.5862 | 0.5789 |
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- | 0.0653 | 13.7931 | 400 | 1.4763 | 0.6207 | 0.5951 |
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  ### Framework versions
 
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  ---
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+ license: cc-by-sa-4.0
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+ base_model: EMBEDDIA/sloberta
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  tags:
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  - generated_from_trainer
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  metrics:
 
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  # fine_tuned_rte_sloberta
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+ This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6653
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  - Accuracy: 0.6207
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+ - F1: 0.5750
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
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+ | 0.6985 | 1.7241 | 50 | 0.7540 | 0.4138 | 0.2422 |
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+ | 0.7058 | 3.4483 | 100 | 0.6912 | 0.5862 | 0.4333 |
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+ | 0.6993 | 5.1724 | 150 | 0.6980 | 0.4138 | 0.2422 |
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+ | 0.7003 | 6.8966 | 200 | 0.6806 | 0.5862 | 0.4333 |
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+ | 0.6968 | 8.6207 | 250 | 0.6730 | 0.5862 | 0.4333 |
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+ | 0.6736 | 10.3448 | 300 | 0.6726 | 0.6897 | 0.6801 |
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+ | 0.6339 | 12.0690 | 350 | 0.6580 | 0.6207 | 0.6090 |
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+ | 0.6005 | 13.7931 | 400 | 0.6653 | 0.6207 | 0.5750 |
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  ### Framework versions
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@@ -1,12 +1,13 @@
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