Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'validation' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Column() changed from object to string in row 0
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
                  obj = self._get_object_parser(self.data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
                  self._parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1402, in _parse
                  self.obj = DataFrame(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/frame.py", line 778, in __init__
                  mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
                  return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
                  index = _extract_index(arrays)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 667, in _extract_index
                  raise ValueError("If using all scalar values, you must pass an index")
              ValueError: If using all scalar values, you must pass an index
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3335, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 476, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 323, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0

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Tokenizer

Imp Links: PyPI Main Library (tokeniser-py) | PyPI Lite Library (tokeniser-py-lite) | Main Library GitHub (tokeniser-py) | Lite Library GitHub (tokeniser-py-lite) | Demo (HF Spaces) | Complete repo (chunked) - GitHub | Imp Files Github

This is a tokeniser created on a custom-written algorithm on a huge vocabulary of ~1B tokens. These tokens are given in the files (such that they are <2GB each, making them trackable by Git LFS). The text corpus is from the SlimPajama dataset by cerebras and consists of the whole text and validation corpus. The final tokeniser is available in two versions (0.5B version - Val. data only and 1B version - Val data + Test data, created using the same algo). The files includes the token counts, the text corpus used, individual lines/paras from SlimPajama as a list JSON, ordered tokeniser with token ids (in order of their counts), unordered tokeniser with token ids.

The tokeniser contains 131,072 tokens.

To do:

  • Write custom code for the final tokenisation part (to break text into tokens)
  • Create a python library for using the tokeniser
  • Put the files on GitHub for a general overview
  • Release the experimentation notebook and the tokenisation code (as part of the library)
  • Make a writeup to explain the algo used

Note: algo used is no industry standard algo like Byte Pair encoding, infact i have not studied BPE yet, i wanted to create a tokeniser from scratch first without any idea of what is currently used, and then compare the two, so a lot of what i implement may have similarities to that in industry but may not provide some performance improvement

I am storing it on HF, not on GITHUB, because of storage issues, and due to easy availability for the AI community.

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