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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text.learner</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..core import ArrayLearner, GenLearner, _load_model
from ..imports import *
from .preprocessor import TransformersPreprocessor


class BERTTextClassLearner(ArrayLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for text classification using Array data.
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None

        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            # BERT-style tuple
            join_char = &#34; &#34;
            obs = val[0][0][idx]
            if preproc is not None:
                obs = preproc.undo(obs)
                if preproc.is_nospace_lang():
                    join_char = &#34;&#34;
            if type(obs) == str:
                obs = join_char.join(obs.split()[:512])
            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
            print(obs)
        return


class TransformerTextClassLearner(GenLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for text classification using Array data.
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None

        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            join_char = &#34; &#34;
            # obs = val.x[idx][0]
            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
        return

    def _prepare(self, data, train=True):
        &#34;&#34;&#34;
        ```
        prepare data as tf.Dataset
        ```
        &#34;&#34;&#34;
        # HF_EXCEPTION
        # convert arrays to TF dataset (iterator) on-the-fly
        # to work around issues with transformers and tf.Datasets
        if data is None:
            return None
        return data.to_tfdataset(train=train)

    def predict(self, val_data=None):
        &#34;&#34;&#34;
        ```
        Makes predictions on validation set
        ```
        &#34;&#34;&#34;
        if val_data is not None:
            val = val_data
        else:
            val = self.val_data
        if val is None:
            raise Exception(&#34;val_data must be supplied to get_learner or predict&#34;)
        if hasattr(val, &#34;reset&#34;):
            val.reset()
        classification, multilabel = U.is_classifier(self.model)
        preds = self.model.predict(self._prepare(val, train=False))
        if hasattr(
            preds, &#34;logits&#34;
        ):  # dep_fix: breaking change in transformers==4.0.0 - also needed for Longformer
            # if type(preds).__name__ == &#39;TFSequenceClassifierOutput&#39;: # dep_fix: undocumented breaking change in transformers==4.0.0
            # REFERENCE: https://discuss.huggingface.co/t/new-model-output-types/195
            preds = preds.logits

        # dep_fix: transformers in TF 2.2.0 returns a tuple insead of NumPy array for some reason
        if isinstance(preds, tuple) and len(preds) == 1:
            preds = preds[0]

        if classification:
            if multilabel:
                return keras.activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
            else:
                return keras.activations.softmax(tf.convert_to_tensor(preds)).numpy()
        else:
            return preds

    def save_model(self, fpath):
        &#34;&#34;&#34;
        ```
        save Transformers model
        ```
        &#34;&#34;&#34;
        self._make_model_folder(fpath)
        self.model.save_pretrained(fpath)
        return

    # 2020-07-07: removed, as core.Learner.load_model calls TransformerPreprocessor.load_model_and_configure
    # def load_model(self, fpath, preproc=None):
    #    &#34;&#34;&#34;
    #    load Transformers model
    #    Args:
    #      fpath(str): path to folder containing model files
    #      preproc(TransformerPreprocessor): a TransformerPreprocessor instance.
    #    &#34;&#34;&#34;
    #    if preproc is None or not isinstance(preproc, TransformersPreprocessor):
    #        raise ValueError(&#39;preproc arg is required to load Transformer models from disk. &#39; +\
    #                          &#39;Supply a TransformersPreprocessor instance. This is &#39; +\
    #                          &#39;either the third return value from texts_from* function or &#39;+\
    #                          &#39;the result of calling ktrain.text.Transformer&#39;)

    #    self.model = _load_model(fpath, preproc=preproc)
    #    return</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.text.learner.BERTTextClassLearner"><code class="flex name class">
<span>class <span class="ident">BERTTextClassLearner</span></span>
<span>(</span><span>model, train_data=None, val_data=None, batch_size=32, eval_batch_size=32, workers=1, use_multiprocessing=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Main class used to tune and train Keras models for text classification using Array data.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class BERTTextClassLearner(ArrayLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for text classification using Array data.
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None

        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            # BERT-style tuple
            join_char = &#34; &#34;
            obs = val[0][0][idx]
            if preproc is not None:
                obs = preproc.undo(obs)
                if preproc.is_nospace_lang():
                    join_char = &#34;&#34;
            if type(obs) == str:
                obs = join_char.join(obs.split()[:512])
            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
            print(obs)
        return</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.core.ArrayLearner" href="../core.html#ktrain.core.ArrayLearner">ArrayLearner</a></li>
<li><a title="ktrain.core.Learner" href="../core.html#ktrain.core.Learner">Learner</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.learner.BERTTextClassLearner.view_top_losses"><code class="name flex">
<span>def <span class="ident">view_top_losses</span></span>(<span>self, n=4, preproc=None, val_data=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Views observations with top losses in validation set.
Args:
 n(int or tuple): a range to select in form of int or tuple
                  e.g., n=8 is treated as n=(0,8)
 preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                         For some data like text data, a preprocessor
                         is required to undo the pre-processing
                         to correctly view raw data.
  val_data:  optional val_data to use instead of self.val_data
Returns:
    list of n tuples where first element is either
    filepath or id of validation example and second element
    is loss.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def view_top_losses(self, n=4, preproc=None, val_data=None):
    &#34;&#34;&#34;
    ```
    Views observations with top losses in validation set.
    Args:
     n(int or tuple): a range to select in form of int or tuple
                      e.g., n=8 is treated as n=(0,8)
     preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                             For some data like text data, a preprocessor
                             is required to undo the pre-processing
                             to correctly view raw data.
      val_data:  optional val_data to use instead of self.val_data
    Returns:
        list of n tuples where first element is either
        filepath or id of validation example and second element
        is loss.
    ```
    &#34;&#34;&#34;
    val = self._check_val(val_data)

    # get top losses and associated data
    tups = self.top_losses(n=n, val_data=val, preproc=preproc)

    # get multilabel status and class names
    classes = preproc.get_classes() if preproc is not None else None

    # iterate through losses
    for tup in tups:

        # get data
        idx = tup[0]
        loss = tup[1]
        truth = tup[2]
        pred = tup[3]

        # BERT-style tuple
        join_char = &#34; &#34;
        obs = val[0][0][idx]
        if preproc is not None:
            obs = preproc.undo(obs)
            if preproc.is_nospace_lang():
                join_char = &#34;&#34;
        if type(obs) == str:
            obs = join_char.join(obs.split()[:512])
        print(&#34;----------&#34;)
        print(
            &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
            % (idx, round(loss, 2), truth, pred)
        )
        print(obs)
    return</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.core.ArrayLearner" href="../core.html#ktrain.core.ArrayLearner">ArrayLearner</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.core.ArrayLearner.autofit" href="../core.html#ktrain.core.Learner.autofit">autofit</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.evaluate" href="../core.html#ktrain.core.Learner.evaluate">evaluate</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.fit" href="../core.html#ktrain.core.ArrayLearner.fit">fit</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.fit_onecycle" href="../core.html#ktrain.core.Learner.fit_onecycle">fit_onecycle</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.freeze" href="../core.html#ktrain.core.Learner.freeze">freeze</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.get_weight_decay" href="../core.html#ktrain.core.Learner.get_weight_decay">get_weight_decay</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.layer_output" href="../core.html#ktrain.core.ArrayLearner.layer_output">layer_output</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.load_model" href="../core.html#ktrain.core.Learner.load_model">load_model</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.lr_estimate" href="../core.html#ktrain.core.Learner.lr_estimate">lr_estimate</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.lr_find" href="../core.html#ktrain.core.Learner.lr_find">lr_find</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.lr_plot" href="../core.html#ktrain.core.Learner.lr_plot">lr_plot</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.plot" href="../core.html#ktrain.core.Learner.plot">plot</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.predict" href="../core.html#ktrain.core.Learner.predict">predict</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.print_layers" href="../core.html#ktrain.core.Learner.print_layers">print_layers</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.reset_weights" href="../core.html#ktrain.core.Learner.reset_weights">reset_weights</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.save_model" href="../core.html#ktrain.core.Learner.save_model">save_model</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.set_model" href="../core.html#ktrain.core.Learner.set_model">set_model</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.set_weight_decay" href="../core.html#ktrain.core.Learner.set_weight_decay">set_weight_decay</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.top_losses" href="../core.html#ktrain.core.Learner.top_losses">top_losses</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.unfreeze" href="../core.html#ktrain.core.Learner.unfreeze">unfreeze</a></code></li>
<li><code><a title="ktrain.core.ArrayLearner.validate" href="../core.html#ktrain.core.Learner.validate">validate</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="ktrain.text.learner.TransformerTextClassLearner"><code class="flex name class">
<span>class <span class="ident">TransformerTextClassLearner</span></span>
<span>(</span><span>model, train_data=None, val_data=None, batch_size=32, eval_batch_size=32, workers=1, use_multiprocessing=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Main class used to tune and train Keras models for text classification using Array data.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TransformerTextClassLearner(GenLearner):
    &#34;&#34;&#34;
    ```
    Main class used to tune and train Keras models for text classification using Array data.
    ```
    &#34;&#34;&#34;

    def __init__(
        self,
        model,
        train_data=None,
        val_data=None,
        batch_size=U.DEFAULT_BS,
        eval_batch_size=U.DEFAULT_BS,
        workers=1,
        use_multiprocessing=False,
    ):
        super().__init__(
            model,
            train_data=train_data,
            val_data=val_data,
            batch_size=batch_size,
            eval_batch_size=eval_batch_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )
        return

    def view_top_losses(self, n=4, preproc=None, val_data=None):
        &#34;&#34;&#34;
        ```
        Views observations with top losses in validation set.
        Args:
         n(int or tuple): a range to select in form of int or tuple
                          e.g., n=8 is treated as n=(0,8)
         preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                                 For some data like text data, a preprocessor
                                 is required to undo the pre-processing
                                 to correctly view raw data.
          val_data:  optional val_data to use instead of self.val_data
        Returns:
            list of n tuples where first element is either
            filepath or id of validation example and second element
            is loss.
        ```
        &#34;&#34;&#34;
        val = self._check_val(val_data)

        # get top losses and associated data
        tups = self.top_losses(n=n, val_data=val, preproc=preproc)

        # get multilabel status and class names
        classes = preproc.get_classes() if preproc is not None else None

        # iterate through losses
        for tup in tups:

            # get data
            idx = tup[0]
            loss = tup[1]
            truth = tup[2]
            pred = tup[3]

            join_char = &#34; &#34;
            # obs = val.x[idx][0]
            print(&#34;----------&#34;)
            print(
                &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
                % (idx, round(loss, 2), truth, pred)
            )
        return

    def _prepare(self, data, train=True):
        &#34;&#34;&#34;
        ```
        prepare data as tf.Dataset
        ```
        &#34;&#34;&#34;
        # HF_EXCEPTION
        # convert arrays to TF dataset (iterator) on-the-fly
        # to work around issues with transformers and tf.Datasets
        if data is None:
            return None
        return data.to_tfdataset(train=train)

    def predict(self, val_data=None):
        &#34;&#34;&#34;
        ```
        Makes predictions on validation set
        ```
        &#34;&#34;&#34;
        if val_data is not None:
            val = val_data
        else:
            val = self.val_data
        if val is None:
            raise Exception(&#34;val_data must be supplied to get_learner or predict&#34;)
        if hasattr(val, &#34;reset&#34;):
            val.reset()
        classification, multilabel = U.is_classifier(self.model)
        preds = self.model.predict(self._prepare(val, train=False))
        if hasattr(
            preds, &#34;logits&#34;
        ):  # dep_fix: breaking change in transformers==4.0.0 - also needed for Longformer
            # if type(preds).__name__ == &#39;TFSequenceClassifierOutput&#39;: # dep_fix: undocumented breaking change in transformers==4.0.0
            # REFERENCE: https://discuss.huggingface.co/t/new-model-output-types/195
            preds = preds.logits

        # dep_fix: transformers in TF 2.2.0 returns a tuple insead of NumPy array for some reason
        if isinstance(preds, tuple) and len(preds) == 1:
            preds = preds[0]

        if classification:
            if multilabel:
                return keras.activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
            else:
                return keras.activations.softmax(tf.convert_to_tensor(preds)).numpy()
        else:
            return preds

    def save_model(self, fpath):
        &#34;&#34;&#34;
        ```
        save Transformers model
        ```
        &#34;&#34;&#34;
        self._make_model_folder(fpath)
        self.model.save_pretrained(fpath)
        return</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></li>
<li><a title="ktrain.core.Learner" href="../core.html#ktrain.core.Learner">Learner</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.text.learner.TransformerTextClassLearner.save_model"><code class="name flex">
<span>def <span class="ident">save_model</span></span>(<span>self, fpath)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>save Transformers model
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def save_model(self, fpath):
    &#34;&#34;&#34;
    ```
    save Transformers model
    ```
    &#34;&#34;&#34;
    self._make_model_folder(fpath)
    self.model.save_pretrained(fpath)
    return</code></pre>
</details>
</dd>
<dt id="ktrain.text.learner.TransformerTextClassLearner.view_top_losses"><code class="name flex">
<span>def <span class="ident">view_top_losses</span></span>(<span>self, n=4, preproc=None, val_data=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Views observations with top losses in validation set.
Args:
 n(int or tuple): a range to select in form of int or tuple
                  e.g., n=8 is treated as n=(0,8)
 preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                         For some data like text data, a preprocessor
                         is required to undo the pre-processing
                         to correctly view raw data.
  val_data:  optional val_data to use instead of self.val_data
Returns:
    list of n tuples where first element is either
    filepath or id of validation example and second element
    is loss.
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def view_top_losses(self, n=4, preproc=None, val_data=None):
    &#34;&#34;&#34;
    ```
    Views observations with top losses in validation set.
    Args:
     n(int or tuple): a range to select in form of int or tuple
                      e.g., n=8 is treated as n=(0,8)
     preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
                             For some data like text data, a preprocessor
                             is required to undo the pre-processing
                             to correctly view raw data.
      val_data:  optional val_data to use instead of self.val_data
    Returns:
        list of n tuples where first element is either
        filepath or id of validation example and second element
        is loss.
    ```
    &#34;&#34;&#34;
    val = self._check_val(val_data)

    # get top losses and associated data
    tups = self.top_losses(n=n, val_data=val, preproc=preproc)

    # get multilabel status and class names
    classes = preproc.get_classes() if preproc is not None else None

    # iterate through losses
    for tup in tups:

        # get data
        idx = tup[0]
        loss = tup[1]
        truth = tup[2]
        pred = tup[3]

        join_char = &#34; &#34;
        # obs = val.x[idx][0]
        print(&#34;----------&#34;)
        print(
            &#34;id:%s | loss:%s | true:%s | pred:%s)\n&#34;
            % (idx, round(loss, 2), truth, pred)
        )
    return</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.core.GenLearner" href="../core.html#ktrain.core.GenLearner">GenLearner</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.core.GenLearner.autofit" href="../core.html#ktrain.core.Learner.autofit">autofit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.evaluate" href="../core.html#ktrain.core.Learner.evaluate">evaluate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit" href="../core.html#ktrain.core.GenLearner.fit">fit</a></code></li>
<li><code><a title="ktrain.core.GenLearner.fit_onecycle" href="../core.html#ktrain.core.Learner.fit_onecycle">fit_onecycle</a></code></li>
<li><code><a title="ktrain.core.GenLearner.freeze" href="../core.html#ktrain.core.Learner.freeze">freeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.get_weight_decay" href="../core.html#ktrain.core.Learner.get_weight_decay">get_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.layer_output" href="../core.html#ktrain.core.GenLearner.layer_output">layer_output</a></code></li>
<li><code><a title="ktrain.core.GenLearner.load_model" href="../core.html#ktrain.core.Learner.load_model">load_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_estimate" href="../core.html#ktrain.core.Learner.lr_estimate">lr_estimate</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_find" href="../core.html#ktrain.core.Learner.lr_find">lr_find</a></code></li>
<li><code><a title="ktrain.core.GenLearner.lr_plot" href="../core.html#ktrain.core.Learner.lr_plot">lr_plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.plot" href="../core.html#ktrain.core.Learner.plot">plot</a></code></li>
<li><code><a title="ktrain.core.GenLearner.predict" href="../core.html#ktrain.core.Learner.predict">predict</a></code></li>
<li><code><a title="ktrain.core.GenLearner.print_layers" href="../core.html#ktrain.core.Learner.print_layers">print_layers</a></code></li>
<li><code><a title="ktrain.core.GenLearner.reset_weights" href="../core.html#ktrain.core.Learner.reset_weights">reset_weights</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_model" href="../core.html#ktrain.core.Learner.set_model">set_model</a></code></li>
<li><code><a title="ktrain.core.GenLearner.set_weight_decay" href="../core.html#ktrain.core.Learner.set_weight_decay">set_weight_decay</a></code></li>
<li><code><a title="ktrain.core.GenLearner.top_losses" href="../core.html#ktrain.core.Learner.top_losses">top_losses</a></code></li>
<li><code><a title="ktrain.core.GenLearner.unfreeze" href="../core.html#ktrain.core.Learner.unfreeze">unfreeze</a></code></li>
<li><code><a title="ktrain.core.GenLearner.validate" href="../core.html#ktrain.core.Learner.validate">validate</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.text" href="index.html">ktrain.text</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.text.learner.BERTTextClassLearner" href="#ktrain.text.learner.BERTTextClassLearner">BERTTextClassLearner</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.learner.BERTTextClassLearner.view_top_losses" href="#ktrain.text.learner.BERTTextClassLearner.view_top_losses">view_top_losses</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="ktrain.text.learner.TransformerTextClassLearner" href="#ktrain.text.learner.TransformerTextClassLearner">TransformerTextClassLearner</a></code></h4>
<ul class="">
<li><code><a title="ktrain.text.learner.TransformerTextClassLearner.save_model" href="#ktrain.text.learner.TransformerTextClassLearner.save_model">save_model</a></code></li>
<li><code><a title="ktrain.text.learner.TransformerTextClassLearner.view_top_losses" href="#ktrain.text.learner.TransformerTextClassLearner.view_top_losses">view_top_losses</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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