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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 ..imports import *
from .. import utils as U
from ..core import ArrayLearner, GenLearner, _load_model
from .preprocessor import TransformersPreprocessor
class BERTTextClassLearner(ArrayLearner):
"""
```
Main class used to tune and train Keras models for text classification using Array data.
```
"""
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):
"""
```
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.
```
"""
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 = ' '
obs = val[0][0][idx]
if preproc is not None:
obs = preproc.undo(obs)
if preproc.is_nospace_lang(): join_char = ''
if type(obs) == str:
obs = join_char.join(obs.split()[:512])
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (idx, round(loss,2), truth, pred))
print(obs)
return
class TransformerTextClassLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for text classification using Array data.
```
"""
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):
"""
```
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.
```
"""
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 = ' '
#obs = val.x[idx][0]
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (idx, round(loss,2), truth, pred))
return
def _prepare(self, data, train=True):
"""
```
prepare data as tf.Dataset
```
"""
# 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):
"""
```
Makes predictions on validation set
```
"""
if val_data is not None:
val = val_data
else:
val = self.val_data
if val is None: raise Exception('val_data must be supplied to get_learner or predict')
if hasattr(val, 'reset'): val.reset()
classification, multilabel = U.is_classifier(self.model)
preds = self.model.predict(self._prepare(val, train=False))
if hasattr(preds, 'logits'): # dep_fix: breaking change in transformers==4.0.0 - also needed for Longformer
#if type(preds).__name__ == 'TFSequenceClassifierOutput': # 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 activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
else:
return activations.softmax(tf.convert_to_tensor(preds)).numpy()
else:
return preds
def save_model(self, fpath):
"""
```
save Transformers model
```
"""
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):
# """
# load Transformers model
# Args:
# fpath(str): path to folder containing model files
# preproc(TransformerPreprocessor): a TransformerPreprocessor instance.
# """
# if preproc is None or not isinstance(preproc, TransformersPreprocessor):
# raise ValueError('preproc arg is required to load Transformer models from disk. ' +\
# 'Supply a TransformersPreprocessor instance. This is ' +\
# 'either the third return value from texts_from* function or '+\
# 'the result of calling ktrain.text.Transformer')
# 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):
"""
```
Main class used to tune and train Keras models for text classification using Array data.
```
"""
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):
"""
```
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.
```
"""
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 = ' '
obs = val[0][0][idx]
if preproc is not None:
obs = preproc.undo(obs)
if preproc.is_nospace_lang(): join_char = ''
if type(obs) == str:
obs = join_char.join(obs.split()[:512])
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (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):
"""
```
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.
```
"""
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 = ' '
obs = val[0][0][idx]
if preproc is not None:
obs = preproc.undo(obs)
if preproc.is_nospace_lang(): join_char = ''
if type(obs) == str:
obs = join_char.join(obs.split()[:512])
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (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):
"""
```
Main class used to tune and train Keras models for text classification using Array data.
```
"""
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):
"""
```
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.
```
"""
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 = ' '
#obs = val.x[idx][0]
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (idx, round(loss,2), truth, pred))
return
def _prepare(self, data, train=True):
"""
```
prepare data as tf.Dataset
```
"""
# 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):
"""
```
Makes predictions on validation set
```
"""
if val_data is not None:
val = val_data
else:
val = self.val_data
if val is None: raise Exception('val_data must be supplied to get_learner or predict')
if hasattr(val, 'reset'): val.reset()
classification, multilabel = U.is_classifier(self.model)
preds = self.model.predict(self._prepare(val, train=False))
if hasattr(preds, 'logits'): # dep_fix: breaking change in transformers==4.0.0 - also needed for Longformer
#if type(preds).__name__ == 'TFSequenceClassifierOutput': # 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 activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
else:
return activations.softmax(tf.convert_to_tensor(preds)).numpy()
else:
return preds
def save_model(self, fpath):
"""
```
save Transformers model
```
"""
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):
"""
```
save Transformers model
```
"""
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):
"""
```
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.
```
"""
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 = ' '
#obs = val.x[idx][0]
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (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>
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