gBERt_base_twitter_sentiment_politicians

This gBert-base model was finetuned on a sentiment prediction task with tweets from German politician during the German Federal Election in 2021.

Model Description:

This model was trained on ~30.000 annotated tweets in German language on its sentiment. It can predict tweets as negative, positive or neutral. It achieved an accuracy of 93% on the specific dataset.

Model Implementation

You can implement this model for example with Simpletransformers. First you have to unpack the file.

def unpack_model(model_name=''):
  tar = tarfile.open(f"{model_name}.tar.gz", "r:gz")
  tar.extractall()
  tar.close()
  

The hyperparameter were defined as follows: train_args ={"reprocess_input_data": True, "fp16":False, "num_train_epochs": 4, "overwrite_output_dir":True, "train_batch_size": 32, "eval_batch_size": 32}

Now create the model: unpack_model(YOUR_DOWNLOADED_FILE_HERE)

model = ClassificationModel(
    "bert", "content/outputs/",
    num_labels= 3,
    args=train_args
)

In this case for the output:

• 0 = positive
• 1 = negative
• 2 = neutral

Example for a positive prediction:

model.predict(["Das ist gut! Wir danken dir."])
([0], array([[ 2.06561327, -3.57908797,  1.5340755 ]]))

Example for a negative prediction:

model.predict(["Ich hasse dich!"])
([1], array([[-3.50486898,  4.29590368, -0.9000684 ]]))

Example for a neutral prediction:

model.predict(["Heute ist Sonntag."])
([2], array([[-2.94458342, -2.91875601,  4.94414234]]))

This model was created by Maximilian Weissenbacher for a project at the University of Regensburg.