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 * **Train and Evaluation Losses** Scale-dependent, percentage and scale independent errors, and parametric likelihoods.
 * **Automatic Model Selection** Parallelized automatic hyperparameter tuning, that efficiently searches best validation configuration.
 * **Simple Interface** Unified SKLearn Interface for `StatsForecast` and `MLForecast` compatibility.
-* **Model Collection**: Out of the box implementation of `MLP`, `LSTM`, `RNN`, `TCN`, `DilatedRNN`, `NBEATS`, `NHITS`, `ESRNN`, `Informer`, `TFT`, `Informer`, `PatchTST` and `HINT`. See the entire [collection here](https://nixtla.github.io/neuralforecast/models.html).
+* **Model Collection**: Out of the box implementation of `MLP`, `LSTM`, `RNN`, `TCN`, `DilatedRNN`, `NBEATS`, `NHITS`, `ESRNN`, `TFT`, `Informer`, `PatchTST` and `HINT`. See the entire [collection here](https://nixtla.github.io/neuralforecast/models.html).
 
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