Default algorithm used in prodigy train ner command

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Hi Ines,

Thank you for the video suggestion.

In my case, I used the prodigy 1.10.8 and spacy 2.3.5 as the environment in my project. I am trying to explore the spacy code here to learn more about the algorithm that is used in the prodigy train ner or spacy train CLI, but I didn't find the precise code that fits with the documentation about TransitionBasedParser in the new spacy documentation here.

Can you suggest to me where I can find it? Or the model architecture that used in my environment is different?

In my case, I have used this command: prodigy train ner my-dataset blank:id --output my-model-name --eval-split 0.2 --n-iter 50 and here is the content of cfg file in the output model:

{
  "beam_width":1,
  "beam_density":0.0,
  "beam_update_prob":1.0,
  "cnn_maxout_pieces":3,
  "nr_feature_tokens":6,
  "nr_class":46,
  "hidden_depth":1,
  "token_vector_width":96,
  "hidden_width":64,
  "maxout_pieces":2,
  "pretrained_vectors":null,
  "bilstm_depth":0,
  "self_attn_depth":0,
  "conv_depth":4,
  "conv_window":1,
  "embed_size":2000
}

It's really important to me to know the algorithm used behind the prodigy train ner in my project environment.

Thank you