Bug Dark Dragon Electric Fairy Fighting Fire Flying Ghost Grass Ground Ice Normal Poison Psychic Rock Steel Water
DistilBERT pokemon model (uncased)
This model is a distilled version of the BERT base model. It was introduced in this paper. The code for the distillation process can be found here. This model is uncased: it does not make a difference between english and English.

Model description

DistilBERT Wikipedia Pokemon model has been fine tuned for sequence classification using data from the notes field of these wikipedia tables. [such as this one](https://en.wikipedia.org/wiki/List_of_generation_III_Pok%C3%A9mon).

Given a pokedex entry as input, the model will return the most likely pokemon-type of the pokemon being described.

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DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives:

Distillation loss: the model was trained to return the same probabilities as the BERT base model.
Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model.
This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks.

Intended uses & limitations
Text Classification.

How to use
You can use this model directly with a pipeline for masked language modeling:

from transformers import pipeline
classifier = pipeline('text-classification', model='mrcoombes/distilbert-wikipedia-pokemon')

classifier("This pokemon likes to attend aquatic parties on midnight rooftops. Their best friend is a dolphin.")

Metrics:
Accuracy 47%.

Limitations, Next Steps and Feedback:

This model could be improved by using over-sampling and under-sampling to reduce class imbalances. The accuracy of a dragon-type pokemon is lower than the accuracy of more well-reprepresented classes within the data. However, works well for well-represented classes.

Happy Classifying 🤗