5roop commited on
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Removed model args from use example

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  1. README.md +17 -21
README.md CHANGED
@@ -31,12 +31,12 @@ model_args = {
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  The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
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- | model | average accuracy | average macro F1|
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- |---|---|---|
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- |bcms-bertic-frenk-hate|0.8313|0.8219|
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- |EMBEDDIA/crosloengual-bert |0.8054|0.796|
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- |xlm-roberta-base |0.7175|0.7049|
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- |fasttext|0.771 |0.754 |
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@@ -44,19 +44,19 @@ From recorded accuracies and macro F1 scores p-values were also calculated:
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  Comparison with `crosloengual-bert`:
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- | test | accuracy p-value | macro F1 p-value|
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- | --- | --- | --- |
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- |Wilcoxon|0.00781|0.00781|
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- |Mann Whithney|0.00108|0.00108|
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- |Student t-test |2.43e-10 |1.27e-10|
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  Comparison with `xlm-roberta-base`:
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- | test | accuracy p-value | macro F1 p-value|
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- | --- | --- | --- |
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- |Wilcoxon|0.00781|0.00781|
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- |Mann Whithney|0.00107|0.00108|
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- |Student t-test |4.83e-11 | 5.61e-11 |
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@@ -64,14 +64,10 @@ Comparison with `xlm-roberta-base`:
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  ```python
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  from simpletransformers.classification import ClassificationModel
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- model_args = {
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- "num_train_epochs": 12,
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- "learning_rate": 1e-5,
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- "train_batch_size": 74}
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  model = ClassificationModel(
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  "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True,
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- args=model_args
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  )
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  The same pipeline was run with two other transformer models and `fasttext` for comparison. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
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+ | model | average accuracy | average macro F1 |
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+ |----------------------------|------------------|------------------|
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+ | bcms-bertic-frenk-hate | 0.8313 | 0.8219 |
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+ | EMBEDDIA/crosloengual-bert | 0.8054 | 0.796 |
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+ | xlm-roberta-base | 0.7175 | 0.7049 |
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+ | fasttext | 0.771 | 0.754 |
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  Comparison with `crosloengual-bert`:
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+ | test | accuracy p-value | macro F1 p-value |
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+ |----------------|------------------|------------------|
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+ | Wilcoxon | 0.00781 | 0.00781 |
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+ | Mann Whithney | 0.00108 | 0.00108 |
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+ | Student t-test | 2.43e-10 | 1.27e-10 |
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  Comparison with `xlm-roberta-base`:
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+ | test | accuracy p-value | macro F1 p-value |
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+ |----------------|------------------|------------------|
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+ | Wilcoxon | 0.00781 | 0.00781 |
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+ | Mann Whithney | 0.00107 | 0.00108 |
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+ | Student t-test | 4.83e-11 | 5.61e-11 |
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  ```python
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  from simpletransformers.classification import ClassificationModel
 
 
 
 
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  model = ClassificationModel(
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  "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True,
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+
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  )
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