This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
Loss: 0.1206
F1: 0.8301
Model description
train_df = pd.read_csv('/content/drive/My Drive/DATASETS/wiki_toxic/train.csv')
validation_df = pd.read_csv('/content/drive/My Drive/DATASETS/wiki_toxic/validation.csv')
#test_df = pd.read_csv('/content/drive/My Drive/wiki_toxic/test.csv')
frac = 0.9
#TRAIN
print(train_df.shape[0]) # get the number of rows in the dataframe
rows_to_delete = train_df.sample(frac=frac, random_state=1)
train_df = train_df.drop(rows_to_delete.index)
print(train_df.shape[0])\
#VALIDATION
print(validation_df.shape[0]) # get the number of rows in the dataframe
rows_to_delete = validation_df.sample(frac=frac, random_state=1)
validation_df = validation_df.drop(rows_to_delete.index)
print(validation_df.shape[0])\
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
