t5-base-dutch

gistfile1.txt

from transformers import AutoTokenizer,T5TokenizerFast,T5Tokenizer
tokenizer = AutoTokenizer.from_pretrained('yhavinga/t5-base-dutch')

reading df = pd.read_csv()

#Below replaces the English text to Dutch
emotion_mapping = {
    'neutral': 'neutraal',
    'joy': 'vreugde',
    'fear': 'angst',
    'anger': 'woede',
    'sadness': 'verdriet',
    'love': 'liefde'
}

df['Category_updated'] = df['Category'].replace(emotion_mapping)

df['Category_updated']


Emotion = [i for i in df_emotion['Category_updated'].unique() ]
emotions = Emotion

for em in emotions:
  print(len(tokenizer.encode(em)))

class T5FineTuner(pl.LightningModule):
  def __init__(self, hparams):
    super(T5FineTuner, self).__init__()
    self.save_hyperparameters(hparams)
    self.model = T5ForConditionalGeneration.from_pretrained(hparams.model_name_or_path)
    self.tokenizer = T5Tokenizer.from_pretrained(hparams.tokenizer_name_or_path)
    self.validation_step_outputs = []
    self.losses = []
    self.learning_rate = hparams.learning_rate

args_dict = dict(
    data_dir="",  # path for data files
    output_dir="",  # path to save the checkpoints
    model_name_or_path="yhavinga/t5-base-dutch",
    tokenizer_name_or_path="yhavinga/t5-base-dutch",
    max_seq_length=512,
    learning_rate=3e-4,
    weight_decay=0.0,
    adam_epsilon=1e-8,
    warmup_steps=0,
    train_batch_size=8,
    eval_batch_size=8,
    num_train_epochs=2,
    gradient_accumulation_steps=16,
    n_gpu=1,
    fp_16=False,  # Set to True if using mixed-precision training with apex
    max_grad_norm=1.0,
    seed=42,
)

args_dict.update({'data_dir': "Emotions", 'output_dir': "t5_emotion", 'num_train_epochs':2})
args = argparse.Namespace(**args_dict)
print(args_dict)

train_params = dict(
    accumulate_grad_batches=args.gradient_accumulation_steps,
    max_epochs=args.num_train_epochs,
    devices=args.n_gpu,
    precision= 16 if args.fp_16 else 32,
    gradient_clip_val=args.max_grad_norm,
    callbacks=[LoggingCallback()],
)

model = T5FineTuner(args)