isi-nlp / nlcodec Goto Github PK

• Instead of UNK-ing OOV characters, support bytefallback
• character coverage (e..g. 99.95%) param can be used to decide what portion of characters (1-0.9995) to do bytefallback

Currently, char, word, bpe, class schemes are supported

TODO: add byte scheme;
char scheme is Unicode code point, whereas the byte scheme is UTF-8 byte.

Challenges:

• Can this work as 1byte integers? Can sequences be put into a byte array?
• It'd be fairly easy to represent using 2byte ints, but if we can make it work by using only 1byte ints while also keeping extra 4 special types ( such as <s> </s> <cls> <pad>), then it'd be genius!

here is how to reproduce

echo "a1212121212b a1212121212b" | nlcodec learn -l bpe -vs 100 -mf 1 -m /dev/stdout

One would think that BPEScheme.name == "bpe" should return resolve as True given

class BPEScheme(CharScheme):
...
    @property
    @classmethod
    def name(cls):
        return "bpe"

but...

Traceback (most recent call last):
  File "/Users/ljferrer/miniconda3/envs/rtg/lib/python3.7/multiprocessing/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "/Users/ljferrer/miniconda3/envs/rtg/lib/python3.7/multiprocessing/pool.py", line 44, in mapstar
    return list(map(*args))
  File "/Users/ljferrer/Documents/PycharmProjects/rtg/rtg/data/dataset.py", line 215, in __call__
    record = [tokr(col) for col, tokr in zip(record, self.tokenizers)]
  File "/Users/ljferrer/Documents/PycharmProjects/rtg/rtg/data/dataset.py", line 215, in <listcomp>
    record = [tokr(col) for col, tokr in zip(record, self.tokenizers)]
  File "/Users/ljferrer/Documents/PycharmProjects/rtg/rtg/data/codec.py", line 171, in encode_as_ids
    if self.codec.name == "bpe" and split_ratio > 0:
TypeError: 'classmethod' object is not callable

• save the flag and normalization type in model file (during training)
• apply it consistently during encoding

nlcodec doesn't install pyspark but requires it. Installing pyspark separately worked.

  File "/home1/jain593/.conda/envs/nmt_toolkits_rtg/lib/python3.7/site-packages/nlcodec/term_freq.py", line 10, in <module>
    from nlcodec import spark as spark_util
  File "/home1/jain593/.conda/envs/nmt_toolkits_rtg/lib/python3.7/site-packages/nlcodec/spark.py", line 12, in <module>
    from pyspark.sql import DataFrame, SparkSession
ModuleNotFoundError: No module named 'pyspark'

related to #1

https://github.com/thammegowda/mtdata/blob/master/.travis.yml

Use __slots__ for data classes such as Nodes in linkedlist, trie, etc

Traceback (most recent call last):
  File "/Users/tg/miniconda3/envs/rtg/bin/nlcodec", line 5, in <module>
    from nlcodec.__main__ import mainnlcodec
ImportError: cannot import name 'mainnlcodec' from 'nlcodec.__main__' (/Users/tg/miniconda3/envs/rtg/lib/python3.7/site-packages/nlcodec/__main__.py)

Caused by: missing a comma

Frequency count of reserved types help for computing imbalance

Keep certain characters separate; don't merge them even if there is sufficient frequency

1. digits
2. punctuations
3. dates months years
4. ... anything else?

watch out: be language agnostic. use Unicode table to figure out digit/punch annotation

  File "...nlcodec/codec.py", line 151, in __init__
    assert len(self.idx_to_str) == len(self.str_to_idx)

clearly, this is a dumb assumption.
In BPE, we have more that one "legal or valid" way of encoding strings to int seqs.

https://github.com/thammegowda/bpepp/blob/aff1164e84ec2b5cee28aa9d97c1eb71e8ca1a09/bpepp.py#L557

dont drop seqs,
use them by slightly editing them

example

4796	comed▁	1	2316	318 202
4797	welcomed▁	1	2316	585 4796

related to https://github.com/isi-nlp/rtg/issues/197

Often we will have to encode a huge dataset (say training data for NMT) in one row.
Encoding can be trivially parallelized using multiprocessing, with the help of a pool of worker processes.

1. Accept a list of files
2. compute term frequencies
3. Eliminate types with zero counts
4. Preserve reserved types even if they have zero counts
5. Save the resulting model at a given file path
6. Return index mapping between old and new, so we can go back to model and shrink embedding tables

Signature should be something like : scheme.fit(files:List[Path], min_freq:int=1, save_at:Path=None) -> List[int]

Also, for the future work, think about adding a new set of types to vocab. Model can insert a few rows with random weights or average of remaining rows.