class_docstr: Basic wrapper around several `DataLoader`s with factory methods for NLP problems
class_docstr_tok: ["Basic","wrapper","around","several","`","DataLoader","`","s","with","factory","methods","for","NLP","problems"]
docstr: Create from `df` in `path` with `valid_pct`
docstr_tok: ["Create","from","`","df","`","in","`","path","`","with","`","valid_pct","`"]
code: def from_df(
      cls,
      df,
      path=".",
      valid_pct=0.2,
      seed=None,
      text_col=0,
      label_col=1,
      label_delim=None,
      y_block=None,
      text_vocab=None,
      is_lm=False,
      valid_col=None,
      tok_tfm=None,
      tok_text_col="text",
      seq_len=72,
      backwards=False,
      **kwargs,
  ):
      "Create from `df` in `path` with `valid_pct`"
      blocks = [
          TextBlock.from_df(
              text_col,
              text_vocab,
              is_lm,
              seq_len,
              backwards,
              tok=tok_tfm,
          )
      ]
      if y_block is None and not is_lm:
          blocks.append(
              MultiCategoryBlock
              if is_listy(label_col) and len(label_col) > 1
              else CategoryBlock
          )
      if y_block is not None and not is_lm:
          blocks += y_block if is_listy(y_block) else [y_block]
      splitter = (
          RandomSplitter(valid_pct, seed=seed)
          if valid_col is None
          else ColSplitter(valid_col)
      )
      dblock = DataBlock(
          blocks=blocks,
          get_x=ColReader(tok_text_col),
          get_y=None
          if is_lm
          else ColReader(label_col, label_delim=label_delim),
          splitter=splitter,
      )
      return cls.from_dblock(
          dblock, df, path=path, seq_len=seq_len, **kwargs
      )
  
imports: from __future__ import annotations
  from ..torch_basics import *
  from ..data.all import *
  from .core import *
label_desc: Use image node_id 2 for calling the TextDataLoaders obj's underlying member method code with example usage: obj.from_df(cls, df, path, valid_pct, seed, text_col, label_col, label_delim, y_block, text_vocab, is_lm, valid_col, tok_tfm, tok_text_col, seq_len, backwards) and returns: cls