At least for me, there's large value in consuming bigger volumes of Chinese to get me used to pattern-matching on the characters, as opposed to only reading a smaller amount of harder characters that I'm less likely to actually encounter
[see https://news.ycombinator.com/item?id=45988611 for explanation]
I recently had some trouble converting a HF transformer I trained with PyTorch to Core ML. I just couldn’t get the KV cache to work, which made it unusably slow after 50 tokens…
I’m trying to learn to speak Chinese and not read it yet. The issue is most of the language learning apps have a focus on characters. I feel like I just want to see the pinyin. Maybe I don’t know what I need, but I haven’t found the right tool.
My first concerns though:
1. How can the system know which words I already know.
2. To what degree will I misunderstand the meaning of words.
3. Somewhat related to 2, how inaccurate will be description / explanation of words be.
1. How does it know which words I already know? It doesn’t automatically. You provide that set. For example, if you’ve completed HSK 1, you can paste the HSK 1 word list into LangSeed and mark those as "known". From there, new explanations are constrained to that vocabulary. You can also paste in real text and mark the easy words as known, though that’s a bit more manual.
2. How much might I misunderstand word meanings? Depends on how advanced the vocab is and how large your known-word set is. I think of this as building intuition rather than giving dictionary-precise definitions. As you see words in more contexts, that intuition sharpens. This is just my experience from testing it over the last couple of weeks.
3. How inaccurate are the explanations? I tested it on Swedish (my native language). There are occasional awkward or slightly odd phrasings, but it’s rarely outright wrong.