Sequence Classification Tuning

class SequenceClassificationDatasets[source]

SequenceClassificationDatasets(train_dset:Dataset, valid_dset:Dataset, tokenizer_name:str, tokenize:bool, tokenize_kwargs:dict, auto_kwargs:dict, remove_columns:list, categorize:MultiCategorize'>]) :: TaskDatasets

A set of datasets designed for sequence classification

Parameters:

• train_dset : <class 'datasets.arrow_dataset.Dataset'>

  A training dataset

• valid_dset : <class 'datasets.arrow_dataset.Dataset'>

  A validation dataset

• tokenizer_name : <class 'str'>

  The name of a tokenizer

• tokenize : <class 'bool'>

  Whether to tokenize immediatly

• tokenize_kwargs : <class 'dict'>

  kwargs for the tokenize function

• auto_kwargs : <class 'dict'>

  AutoTokenizer.from_pretrained kwargs

• remove_columns : <class 'list'>

  The columns to remove when tokenizing

• categorize : [<class 'adaptnlp.training.core.Categorize'>, <class 'adaptnlp.training.core.MultiCategorize'>]

  A Categorize instance

SequenceClassificationDatasets.from_dfs[source]

SequenceClassificationDatasets.from_dfs(train_df:DataFrame, text_col:str, label_col:str, tokenizer_name:str, tokenize:bool=True, is_multicategory:bool=False, label_delim=' ', valid_df=None, split_func=None, split_pct=0.2, tokenize_kwargs:dict={}, auto_kwargs:dict={})

Builds SequenceClassificationDatasets from a DataFrame or set of DataFrames

Parameters:

• train_df : <class 'pandas.core.frame.DataFrame'>

  A training dataframe

• text_col : <class 'str'>

  The name of the text column

• label_col : <class 'str'>

  The name of the label column

• tokenizer_name : <class 'str'>

  The name of the tokenizer

• tokenize : <class 'bool'>, optional

  Whether to tokenize immediatly

• is_multicategory : <class 'bool'>, optional

  Whether each item has a single label or multiple labels

• label_delim : <class 'str'>, optional

  If `is_multicategory`, how to separate the labels

• valid_df : <class 'NoneType'>, optional

  An optional validation dataframe

• split_func : <class 'NoneType'>, optional

  Optionally a splitting function similar to RandomSplitter

• split_pct : <class 'float'>, optional

  What % to split the train_df

• tokenize_kwargs : <class 'dict'>, optional

  kwargs for the tokenize function

• auto_kwargs : <class 'dict'>, optional

  kwargs for the AutoTokenizer.from_pretrained constructor

SequenceClassificationDatasets.from_csvs[source]

SequenceClassificationDatasets.from_csvs(train_csv:Path, text_col:str, label_col:str, tokenizer_name:str, tokenize:bool=True, is_multicategory:bool=False, label_delim=' ', valid_csv:Path=None, split_func=None, split_pct=0.2, tokenize_kwargs:dict={}, auto_kwargs:dict={}, **kwargs)

Builds SequenceClassificationDatasets from a single csv or set of csvs. A convience constructor for from_dfs

Parameters:

• train_csv : <class 'pathlib.Path'>

  A training csv file

• text_col : <class 'str'>

  The name of the text column

• label_col : <class 'str'>

  The name of the label column

• tokenizer_name : <class 'str'>

  The name of the tokenizer

• tokenize : <class 'bool'>, optional

  Whether to tokenize immediatly

• is_multicategory : <class 'bool'>, optional

  Whether each item has a single label or multiple labels

• label_delim : <class 'str'>, optional

  If `is_multicategory`, how to separate the labels

• valid_csv : <class 'pathlib.Path'>, optional

  An optional validation csv

• split_func : <class 'NoneType'>, optional

  Optionally a splitting function similar to RandomSplitter

• split_pct : <class 'float'>, optional

  What % to split the train_df

• tokenize_kwargs : <class 'dict'>, optional

  kwargs for the tokenize function

• auto_kwargs : <class 'dict'>, optional

  kwargs for the AutoTokenizer.from_pretrained constructor

• kwargs : <class 'inspect._empty'>

SequenceClassificationDatasets.from_folders[source]

SequenceClassificationDatasets.from_folders(train_path:Path, get_label:callable, tokenizer_name:str, tokenize:bool=True, is_multicategory:bool=False, label_delim='_', valid_path:Path=None, split_func=None, split_pct=0.2, tokenize_kwargs:dict={}, auto_kwargs:dict={})

Builds SequenceClassificationDatasets from a folder or groups of folders

Parameters:

• train_path : <class 'pathlib.Path'>

  The path to the training data

• get_label : <built-in function callable>

  A function which grabs the label(s) given a text files `Path`

• tokenizer_name : <class 'str'>

  The name of the tokenizer

• tokenize : <class 'bool'>, optional

  Whether to tokenize immediatly

• is_multicategory : <class 'bool'>, optional

  Whether each item has a single label or multiple labels

• label_delim : <class 'str'>, optional

  if `is_multicategory`, how to separate the labels

• valid_path : <class 'pathlib.Path'>, optional

  The path to the validation data

• split_func : <class 'NoneType'>, optional

  Optionally a splitting function similar to RandomSplitter

• split_pct : <class 'float'>, optional

  What % to split the items in the `train_path`

• tokenize_kwargs : <class 'dict'>, optional

  kwargs for the tokenize function

• auto_kwargs : <class 'dict'>, optional

  kwargs for the AutoTokenizer.from_pretrained constructor

class SequenceClassificationTuner[source]

SequenceClassificationTuner(dls:DataLoaders, model_name:str, tokenizer=None, loss_func=CrossEntropyLoss(), metrics=[<function accuracy at 0x7f4dac07a9d0>, <fastai.metrics.AccumMetric object at 0x7f4da7f6e070>], opt_func=Adam, additional_cbs=None, expose_fastai_api=False, num_classes:int=None, **kwargs) :: AdaptiveTuner

An AdaptiveTuner with good defaults for Sequence Classification tasks

Valid kwargs and defaults:

• lr:float = 0.001
• splitter:function = trainable_params
• cbs:list = None
• path:Path = None
• model_dir:Path = 'models'
• wd:float = None
• wd_bn_bias:bool = False
• train_bn:bool = True
• moms: tuple(float) = (0.95, 0.85, 0.95)

Parameters:

• dls : <class 'fastai.data.core.DataLoaders'>

  A set of DataLoaders

• model_name : <class 'str'>

  A HuggingFace model

• tokenizer : <class 'NoneType'>, optional

  A HuggingFace tokenizer

• loss_func : <class 'fastai.losses.CrossEntropyLossFlat'>, optional

  A loss function

• metrics : <class 'list'>, optional

  Metrics to monitor the training with

• opt_func : <class 'function'>, optional

  A fastai or torch Optimizer

• additional_cbs : <class 'NoneType'>, optional

  Additional Callbacks to have always tied to the Tuner,

• expose_fastai_api : <class 'bool'>, optional

  Whether to expose the fastai API

• num_classes : <class 'int'>, optional

  The number of classes

• kwargs : <class 'inspect._empty'>

SequenceClassificationTuner.predict[source]

SequenceClassificationTuner.predict(text:Union[List[str], str], bs:int=64, detail_level:DetailLevel='low', class_names:list=None)

Predict some text for sequence classification with the currently loaded model

Parameters:

• text : typing.Union[typing.List[str], str]

  Some text or list of texts to do inference with

• bs : <class 'int'>, optional

  A batch size to use for multiple texts

• detail_level : <class 'fastcore.basics.DetailLevel'>, optional

  A detail level to return on the predictions

• class_names : <class 'list'>, optional

  A list of labels

Returns:

• <class 'dict'>

  A dictionary of filtered predictions