All Models and datasets
  - seed: 42

Roberta Large NLI Binary Classification Model
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - num_epochs: 5

Semantic Textual Similarity Binary Classification Model
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - num_epochs: 5

Ensemble Meta Model
  - learning_rate: 2e-05
  - train_batch_size: 128
  - eval_batch_size: 16
  - num_epochs: 3
  

  - overall training time: 309 minutes 30 seconds

Roberta Large NLI Binary Classification Model
  - duration per training epoch: 11 minutes
  - model size: 1.42 GB

Semantic Textual Similarity Binary Classification Model
  - duration per training epoch: 4 minutes 30 seconds
  - model size: 501 MB

Ensamble Meta Model
  - duration per training epoch: 4 minutes
  - model size: 1.92 GB

  - Precision
  - Recall
  - F1-score
  - Accuracy

  The Ensemble Model obtained an F1-score of 91% and an accuracy of 91%.

  Validation set
  - Macro Precision: 91.0%
  - Macro Recall: 91.0%
  - Macro F1-score: 91.0%
  - Weighted Precision: 91.0%
  - Weighted Recall: 91.0%
  - Weighted F1-score: 91.0%
  - accuracy: 91.0%
  - Support: 5389

  Test set
  - Macro Precision: 91.0%
  - Macro Recall: 91.0%
  - Macro F1-score: 91.0%
  - Weighted Precision: 91.0%
  - Weighted Recall: 91.0%
  - Weighted F1-score: 91.0%
  - accuracy: 91.0%
  - Support: 1347
  

  - RAM: at least 10 GB
  - Storage: at least 4GB,
  - GPU: a100 40GB

  - Tensorflow 2.18.0+cu12.4
  - Transformers 4.50.3
  - Pandas 2.2.2
  - NumPy 2.0.2
  - Seaborn 0.13.2
  - Huggingface_hub 0.30.1
  - Matplotlib 3.10.0
  - Scikit-learn 1.6.1