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--- |
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language: |
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- de |
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tags: |
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- Text Classification |
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- sentiment |
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- Simpletransformers |
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- deepset/gbert-base |
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--- |
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This gBert-base model was finetuned on a sentiment prediction task with tweets from German politician during the German Federal Election in 2021. |
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## Model Description: |
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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. |
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## Model Implementation |
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You can implement this model for example with Simpletransformers. First you have to unpack the file. |
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def unpack_model(model_name=''): |
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tar = tarfile.open(f"{model_name}.tar.gz", "r:gz") |
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tar.extractall() |
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tar.close() |
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The hyperparameter were defined as follows: |
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train_args ={"reprocess_input_data": True, |
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"fp16":False, |
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"num_train_epochs": 4, |
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"overwrite_output_dir":True, |
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"train_batch_size": 32, |
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"eval_batch_size": 32} |
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Now create the model: |
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unpack_model(YOUR_DOWNLOADED_FILE_HERE) |
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model = ClassificationModel( |
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"bert", "content/outputs/", |
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num_labels= 3, |
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args=train_args |
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) |
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In this case for the output: |
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- 0 = positive |
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- 1 = negative |
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- 2 = neutral |
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Example for a positive prediction: |
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model.predict(["Das ist gut! Wir danken dir."]) |
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([0], array([[ 2.06561327, -3.57908797, 1.5340755 ]])) |
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Example for a negative prediction: |
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model.predict(["Ich hasse dich!"]) |
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([1], array([[-3.50486898, 4.29590368, -0.9000684 ]])) |
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Example for a neutral prediction: |
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model.predict(["Heute ist Sonntag."]) |
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([2], array([[-2.94458342, -2.91875601, 4.94414234]])) |
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This model was created by Maximilian Weissenbacher for a project at the University of Regensburg. |
