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xlm-roberta-large-finetuned-wolof

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xlm-roberta
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Eval Results
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  1. README.md +9 -7
  2. pytorch_model.bin +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.8378048780487805
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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-large](https://huggingface.co/xlm-roberta-large) on the masakhaner2 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2882
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- - F1: 0.8378
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  ## Model description
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@@ -58,15 +58,17 @@ The following hyperparameters were used during training:
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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: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 0.6633 | 1.0 | 739 | 0.3813 | 0.7486 |
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- | 0.259 | 2.0 | 1478 | 0.2772 | 0.7903 |
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- | 0.1289 | 3.0 | 2217 | 0.2882 | 0.8378 |
 
 
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  ### Framework versions
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 0.8361858190709046
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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-large](https://huggingface.co/xlm-roberta-large) on the masakhaner2 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3771
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+ - F1: 0.8362
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  ## Model description
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.7475 | 1.0 | 739 | 0.4053 | 0.6989 |
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+ | 0.3252 | 2.0 | 1478 | 0.3251 | 0.6653 |
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+ | 0.1983 | 3.0 | 2217 | 0.3703 | 0.8234 |
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+ | 0.1139 | 4.0 | 2956 | 0.3170 | 0.8299 |
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+ | 0.052 | 5.0 | 3695 | 0.3771 | 0.8362 |
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  ### Framework versions
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