@ddaspit partially reviewed 11 files and all commit messages, and made 1 comment.
Reviewable status: all files reviewed, 1 unresolved discussion (waiting on @Enkidu93).

machine/corpora/key_term_row.py line 0 at r1 (raw file):
This file should be named key_term.py.

@Enkidu93 made 3 comments.
Reviewable status: 8 of 21 files reviewed, 1 unresolved discussion (waiting on @ddaspit).

machine/corpora/key_term_row.py line at r1 (raw file):

Done.

machine/translation/huggingface/hugging_face_nmt_model_trainer.py line 362 at r3 (raw file):

            ).tokens()
            src_term_partial_word_tokens.remove("▁")
            src_term_partial_word_tokens.remove("\ufffc")

This is mirroring code in silnlp more-or-less exactly. I made an issue for creating a shared utility function that could do some of this. I also experimented with finding a safe way to be able to do this with non-fast tokenizers. It's something we should look into as needed but I decided that it was taking too much time.

tests/translation/huggingface/test_hugging_face_nmt_model_trainer.py line 130 at r3 (raw file):

        corpus = source_corpus.align_rows(target_corpus)

        terms_corpus = DictionaryTextCorpus(MemoryText("terms", [TextRow("terms", 1, ["telephone"])])).align_rows(

I don't love that this test doesn't really cover whether the terms are affecting the result. I just stuck this in here for code coverage (no exceptions thrown, etc.), but I couldn't adapt our one true fine-tuning test because it uses a non-fast tokenizer. I looked for alternatives but couldn't find anything that works. I did confirm in the debugger that everything was being tokenized properly. Maybe we should consider outputting some kind of artifact in ClearML (?) with the tokenized data so we have something to compare apples-to-apples to the tokenized experiment txt files in silnlp.

@ddaspit partially reviewed 9 files and all commit messages, made 1 comment, and resolved 1 discussion.
Reviewable status: 17 of 26 files reviewed, 1 unresolved discussion (waiting on @Enkidu93).

machine/translation/huggingface/hugging_face_nmt_model_trainer.py line 94 at r4 (raw file):

        training_args: Seq2SeqTrainingArguments,
        corpus: Union[ParallelTextCorpus, Dataset],
        terms_corpus: Optional[Union[ParallelTextCorpus, Dataset]] = None,

I think it would be better to add some type of metadata to a row to indicate what kind of data it is, rather than pass around two separate corpora. It feels more in spirit with the design goals of the corpus API in Machine. We could have a generic metadata dictionary that allows you to set whatever you want. Or, we could add a new "data type" property with a predefined set of values, such as gloss, sentence, section, document, etc. What do you think?

@Enkidu93 made 1 comment.
Reviewable status: 17 of 26 files reviewed, 1 unresolved discussion (waiting on @ddaspit).

machine/translation/huggingface/hugging_face_nmt_model_trainer.py line 94 at r4 (raw file):

Yes, I did think of this. If we wanted to add a field to each row, I think an enum like you mentioned might be the better option. We could add it to each text and then expose it through the row as well (?). There were two reasons I didn't already tackle this:

1. It will add a little complexity to the to_hf_dataset() function as well as to the preprocess_function when we call map() on the dataset.
2. I wondered how much it was worth doing this when we only have two categories. Do we foresee additional text types? Or maybe we don't foresee other text types but do we see ourselves needing some kind of tagging for other attributes? This would affect how it should be implemented.

A competing (or perhaps supplemental) option would be for this function to take something like corpus: List[Tuple[CorpusOptions, Union[ParallelCorpus, Dataset]]] or corpus: List[CorpusConfig]. Then the processing options could be more dynamic - e.g., 'include partial words for corpus 1, tag corpus 2 with such-and-such tag, etc.` - regardless of a text type field and it could be specified by the calling code.