code: def train_model(
      model: Model,
      *,
      train: Sequence[Tuple[str, str]],
      test: Sequence[Tuple[str, str]],
      n_iter: int,
      batch_size: int | thinc.types.Generator = 32,
      learn_rate: float | List[float] | thinc.types.Generator = 0.001,
  ) -> Model:
      """
      Args:
          model
          train
          test
          n_iter
          batch_size
          learn_rate
      """
      # binarize language labels
      # NOTE: thinc seems to require type "float32" arrays for training labels
      # errors otherwise... :/
      lb = sklearn.preprocessing.LabelBinarizer()
      lb.fit([lang for _, lang in train])
      # THIS NEXT LINE IS CRITICAL: we need to save the training class labels
      # but don't want to keep this label binarizer around; so, add it to the model
      model.layers[-1].attrs["classes"] = lb.classes_
  
      Y_train = lb.transform([lang for _, lang in train]).astype(
          "float32"
      )
      Y_test = lb.transform([lang for _, lang in test])
  
      # make sure data is on the right device?
      # Y_train = self.model.ops.asarray(Y_train)
      # Y_test = self.model.ops.asarray(Y_test)
  
      X_train = [text for text, _ in train]
      X_test = [text for text, _ in test]
  
      losser = thinc.api.CategoricalCrossentropy(normalize=True)
      optimizer = thinc.api.Adam(learn_rate)
  
      model.initialize(X=X_train[:10], Y=Y_train[:10])
      print(f"{'epoch':>5}  {'loss':>8}  {'score':>8}")
      # iterate over epochs
      for n in range(n_iter):
          loss = 0.0
          # iterate over batches
          batches = model.ops.multibatch(
              batch_size, X_train, Y_train, shuffle=True
          )
          for X, Y in tqdm(batches, leave=False):
              Yh, backprop = model.begin_update(X)
              dYh, loss_batch = losser(Yh, Y)
              loss += loss_batch
              backprop(dYh)
              model.finish_update(optimizer)
              optimizer.step_schedules()
  
          if optimizer.averages:
              with model.use_params(optimizer.averages):
                  score = evaluate_model(
                      model,
                      X_test=X_test,
                      Y_test=Y_test,
                      batch_size=1000,
                  )
          else:
              score = evaluate_model(
                  model, X_test=X_test, Y_test=Y_test, batch_size=1000
              )
          print(f"{n:>5}  {loss:>8.3f}  {score:>8.3f}")
  
      if optimizer.averages:
          with model.use_params(optimizer.averages):
              pred_langs = models.get_model_preds(
                  model, X_test, model.layers[-1].attrs["classes"]
              )
      else:
          pred_langs = models.get_model_preds(
              model, X_test, model.layers[-1].attrs["classes"]
          )
      true_langs = list(lb.inverse_transform(Y_test))
      print(
          sklearn.metrics.classification_report(true_langs, pred_langs)
      )
      return model
  
file_path: textacy/src/textacy/lang_id/_training.py
imports: from __future__ import annotations
  from typing import Iterable, List, Sequence, Tuple
  import sklearn.preprocessing
  import thinc
  from tqdm import tqdm
  from thinc.api import Model
  from .None import models
label_desc: Use image node_id 1 for calling a global function with example usage: train_model(model) and returns: model