(
    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,
)
return model
Args:
        model
        train
        test
        n_iter
        batch_size
        learn_rate
Args:
        model
        train
        test
        n_iter
        batch_size
        learn_rate
Args : model train test n_iter batch_size learn_rate
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
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 : # 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