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bilingual-sentiment-comparison
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{
  "experiment": "bilingual-sentiment-comparison",
  "sample_size": 30000,
  "best_model": "XLM-RoBERTa",
  "best_f1": 0.695640803447516,
  "metrics": [
    {
      "Metric": "Overall Accuracy",
      "Baseline LSTM": 0.681,
      "XLM-RoBERTa": 0.748
    },
    {
      "Metric": "Macro F1",
      "Baseline LSTM": 0.6194696729841381,
      "XLM-RoBERTa": 0.695640803447516
    },
    {
      "Metric": "Weighted F1",
      "Baseline LSTM": 0.672862679937181,
      "XLM-RoBERTa": 0.7470792508149281
    },
    {
      "Metric": "Negative F1",
      "Baseline LSTM": 0.7376116978066612,
      "XLM-RoBERTa": 0.8036605657237936
    },
    {
      "Metric": "Neutral F1",
      "Baseline LSTM": 0.3525046382189239,
      "XLM-RoBERTa": 0.43844856661045534
    },
    {
      "Metric": "Positive F1",
      "Baseline LSTM": 0.7682926829268293,
      "XLM-RoBERTa": 0.8448132780082988
    },
    {
      "Metric": "English Macro F1",
      "Baseline LSTM": 0.6188346258661638,
      "XLM-RoBERTa": 0.6959503203269959
    },
    {
      "Metric": "Spanish Macro F1",
      "Baseline LSTM": 0.6200182612567712,
      "XLM-RoBERTa": 0.6952735701544848
    }
  ],
  "inference_examples": [
    {
      "Text": "I absolutely love this new translation tool, it works flawlessly!",
      "LSTM Prediction": "Positive",
      "XLM-R Prediction": "Positive"
    },
    {
      "Text": "El servicio al cliente fue terrible y el producto lleg\u00f3 roto.",
      "LSTM Prediction": "Negative",
      "XLM-R Prediction": "Negative"
    },
    {
      "Text": "The movie was okay, not great but not bad either.",
      "LSTM Prediction": "Neutral",
      "XLM-R Prediction": "Neutral"
    },
    {
      "Text": "Esta pel\u00edcula es una obra de arte, me encant\u00f3 cada segundo.",
      "LSTM Prediction": "Positive",
      "XLM-R Prediction": "Positive"
    },
    {
      "Text": "I am not sure how to feel about this update, it has some good and bad aspects.",
      "LSTM Prediction": "Neutral",
      "XLM-R Prediction": "Neutral"
    }
  ],
  "config": {
    "lstm_embedding_dim": 128,
    "lstm_units": 64,
    "max_seq_length": 128,
    "xlmr_model": "xlm-roberta-base",
    "epochs": 3,
    "batch_size": 16,
    "seed": 42
  }
}