{"slug": "best-way-to-convert-long-korean-novel-txt-files-into-a-hugging-face-dataset-for", "title": "Best way to convert long Korean novel TXT files into a Hugging Face dataset for causal LM fine-tuning?", "summary": "A Stack Exchange user asked for the best way to convert long Korean novel TXT files into a Hugging Face dataset for causal LM fine-tuning, and the answer recommends separating corpus storage from sequence construction. The practical default is to store by book or chapter and later chunk into fixed-token blocks using the exact model tokenizer, with no overlap initially and optional metadata like book_id. The answer suggests using the Hugging Face 'text' loader with sample_by='document' or building explicit records, and advises checking token counts and decoded sequences before training.", "body_md": "Some parts seem fairly well established, while others seem less settled:\n\nFor continued pretraining / causal-LM training on novel text, I would separate **how you store the corpus** from **how you construct the fixed-length sequences that the model actually trains on**.\n\nSo, for your specific questions, my default answers would be:\n\n| Question | Practical default | \n| Paragraph, scene, chapter, or fixed tokens per Dataset row? | **Book or chapter for storage; fixed tokens later for training.** They do not need to be the same unit. | \n| Tokenizer-aware 1024/2048-token chunking? | **Yes.** Construct training blocks from token IDs produced by the exact model tokenizer. | \n| Overlap? | **No overlap initially.** Consecutive blocks are the simplest baseline. | \n| Artificial line breaks? | **Repair verified formatting artifacts, but preserve real paragraph/dialogue structure.** | \n| Is `{\"text\": \"...\"}` enough? | **Yes.**`book_id` ,`chapter_id` , etc. are optional but very useful metadata. | \n| Are 20,000+ character rows a problem? | **Not by themselves.** A long storage row does not mean feeding the whole row to the model at once. | \n| Preserve chapter/scene boundaries? | **Preserve them in the canonical corpus.** Training block boundaries do not necessarily have to match them. | \n\n In other words, I would not make the irreversible dataset representation itself a collection of 2048-token fragments. I would keep a clean, reconstructable corpus first, and derive model-specific training blocks from it.\n\nA reasonable first pipeline would be:\n\n```\noriginal TXT files\n    ↓\nconservative cleanup\n    ↓\ncanonical book/chapter records + metadata\n    ↓\ntrain/validation split at an appropriate source boundary\n    ↓\nQwen tokenizer\n    ↓\nexplicit document-boundary token where appropriate\n    ↓\n1024/2048/etc. fixed-token blocks\n    ↓\ncausal-LM labels\n```\n\nThat keeps the original book/chapter structure available if you later change tokenizer, block size, model, packing strategy, or evaluation setup.\n\nThe Hugging Face `text` loader actually supports this distinction directly: it normally samples TXT files line-by-line, but it can also sample by `paragraph` or by entire `document` using `sample_by`. See [Loading text data with Datasets](https://huggingface.co/docs/datasets/main/nlp_load).\n\nFor example, if one TXT file is one book:\n\n``` python\nfrom datasets import load_dataset\nfrom pathlib import Path\n\nfiles = [str(p) for p in sorted(Path(\"novels\").glob(\"*.txt\"))]\n\nds = load_dataset(\n    \"text\",\n    data_files=files,\n    split=\"train\",\n    sample_by=\"document\",\n)\n```\n\nIf you want reliable book/chapter IDs, provenance, ordering information, etc., I would probably build the records explicitly instead:\n\n``` python\nfrom datasets import Dataset\nfrom pathlib import Path\n\nrecords = []\n\nfor path in sorted(Path(\"novels\").glob(\"*.txt\")):\n    text = path.read_text(encoding=\"utf-8\")\n\n    # Only conservative, corpus-verified cleanup here.\n    text = text.replace(\"\\r\\n\", \"\\n\").replace(\"\\r\", \"\\n\")\n\n    records.append({\n        \"book_id\": path.stem,\n        \"text\": text,\n    })\n\nds = Dataset.from_list(records)\n```\n\n`text` is all the language-model objective needs. The extra fields are mainly useful for splitting, inspection, debugging, and rebuilding the tokenized dataset later.\n\nBefore doing a long training run, I would check only a few inexpensive things:\n\n- token counts per book/chapter using the **exact Qwen tokenizer** ;\n- a few before/after excerpts from the line-break cleanup;\n- whether the intended train/validation source units are actually disjoint;\n- a few decoded packed sequences around book boundaries;\n- where the document-boundary tokens ended up;\n- labels at document-boundary and padding positions;\n- how many tokens the final packing procedure drops or pads.\n\nThose checks tend to reveal preprocessing mistakes much more cheaply than discovering them after training.\n\n## \nWhy “Dataset row”, “document”, “training block”, and “attention boundary” are different\n\nThere are really four boundaries here, and they can be chosen independently.\n\n### \n\nThis is mainly an organizational boundary.\n\nFor example:\n\n```\nrow 0 = Book A\nrow 1 = Book B\n```\n\nor:\n\n```\nrow 0 = Book A / Chapter 1\nrow 1 = Book A / Chapter 2\nrow 2 = Book A / Chapter 3\nrow 3 = Book B / Chapter 1\n...\n```\n\nBoth are reasonable Dataset representations.\n\nThe chapter version can be convenient because you can inspect, filter, reorder, or split chapters easily. The book version is simpler and preserves the original source with less preprocessing.\n\n### \n\nThis means: **where should the model be told that one independent document ended and another begins?**\n\nThat does not have to equal the Dataset row boundary.\n\nFor example, you can store chapters separately:\n\n```\nrow: Book A, Chapter 1\nrow: Book A, Chapter 2\nrow: Book A, Chapter 3\n```\n\nwhile treating them as one continuous semantic document:\n\n```\nChapter 1\nChapter 2\nChapter 3\n<end of Book A>\n```\n\nSo choosing “one chapter per row” does **not** imply that every chapter needs a document-end token.\n\nFor your source format—usually one complete book per TXT—**book boundaries are a very natural first choice for independent-document boundaries**. Chapter-level document boundaries are also possible if that is deliberately what you want to model, but it is a separate decision.\n\n### \n\nThis is the compute boundary:\n\n```\ntoken 0 ........ token 2047\ntoken 2048 ..... token 4095\n...\n```\n\nA 2048-token block can start or end in the middle of a chapter. That is not automatically a preprocessing error.\n\nThe simple Hugging Face causal-LM example does essentially:\n\n```\ntokenize\n→ concatenate\n→ split into fixed block_size sequences\n→ labels = input_ids\n```\n\nSee the current [`run_clm.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py).\n\n### \n\nThis is yet another question: if two documents are packed into one physical training sequence, should tokens in the second document be allowed to attend to tokens from the first document?\n\nAn end-of-document token provides a **semantic boundary signal**. Strictly blocking attention across documents requires a different attention-mask/packing mechanism.\n\nI would not make strict cross-document attention isolation a requirement for the first version of this pipeline. It is an advanced packing choice that can be evaluated later if it matters.\n\nThis separation is why I would avoid trying to find one universal answer to:\n\nparagraph vs scene vs chapter vs fixed tokens?\n\nThose units solve different problems.\n\n \n## \nCleaning the artificial line breaks\n\nThis is probably the most corpus-specific part of the problem.\n\nSince you already know that some physical line breaks are formatting artifacts, I would correct those **before token packing**, but only when the rule is reliable enough.\n\nFor example, this:\n\n```\n그는 천천히 문을\n열었다. 아무도\n없었다.\n```\n\nmay genuinely be a hard-wrapped representation of:\n\n```\n그는 천천히 문을 열었다. 아무도 없었다.\n```\n\nBut this:\n\n```\n그는 문을 열었다.\n\n“누구세요?”\n\n아무 대답도 없었다.\n```\n\ncontains useful structural information. Flattening all `\\n` characters would erase paragraph and dialogue formatting that you probably *want* the model to learn.\n\nSo I would avoid a blanket transformation such as:\n\n```\ntext = text.replace(\"\\n\", \" \")\n```\n\nA better approach is:\n\n1. keep the original TXT unchanged;\n2. normalize basic file-level things such as CRLF/LF consistently;\n3. inspect representative sections from several books;\n4. identify patterns that reliably indicate hard wrapping;\n5. repair only those patterns;\n6. compare cleaned and original excerpts before processing the full corpus.\n\nThings that can help diagnose the pattern include:\n\n- whether the preceding line ends in sentence punctuation;\n- whether either line begins/ends with quotation marks;\n- blank-line patterns;\n- typical physical line lengths;\n- chapter headings;\n- scene separators.\n\nBut I would treat these as corpus diagnostics, not as universal Korean grammar rules.\n\nThere is at least a close Korean-novel precedent: the [KoCoNovel](https://github.com/storidient/koconovel) project says its preprocessing corrected **incorrect line breaks** in its Korean novel corpus. That supports treating this as a real preprocessing issue, but it does not provide a universal rule that can safely distinguish every artificial line break from intentional literary formatting.\n\nAlso, the tokenizer will not make this distinction for you. Literal newlines affect the resulting token sequence, so it is better to decide what the canonical text should look like *before* creating training blocks.\n\n \n## \nQwen document boundaries and `<|endoftext|>`\n\nFor Qwen specifically, I would pay attention to document boundaries rather than simply concatenating every book with no separator.\n\nThe Qwen documentation describes `<|endoftext|>` as the **end-of-document (EOD) control token** inserted between documents in packed pretraining sequences:\n\n[Qwen3 Key Concepts — Control Tokens](https://github.com/QwenLM/Qwen3/blob/main/docs/source/getting_started/concepts.md)\n\nSo a simple book-level stream can look like:\n\n```\ntokens for Book A\n<|endoftext|>\ntokens for Book B\n<|endoftext|>\ntokens for Book C\n<|endoftext|>\n```\n\nThen that token stream can independently be divided into 2048-token blocks.\n\nOne important detail is that ordinary raw-text tokenization should not be assumed to add the pretraining document marker automatically. I would make it explicit:\n\n``` python\nfrom transformers import AutoTokenizer\n\ntokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen3-0.6B-Base\")\n\neod_id = tokenizer.convert_tokens_to_ids(\"<|endoftext|>\")\n\nids = tokenizer(\n    text,\n    add_special_tokens=False,\n)[\"input_ids\"]\n\nids.append(eod_id)\n```\n\nIf your Dataset rows are chapters but you want **book-level** document boundaries, do not blindly append EOD after every row. Instead preserve `book_id` / chapter order, concatenate the chapters belonging to one book, and append one EOD when that book ends.\n\nFor example:\n\n```\nBook A / Chapter 1\nBook A / Chapter 2\nBook A / Chapter 3\n<|endoftext|>\nBook B / Chapter 1\n...\n```\n\nThat is one reason why keeping metadata separate from the model-specific token representation is useful.\n\nAlso, EOD does not have to coincide with a 2048-token boundary. A physical block can legitimately contain:\n\n```\n...end of Book A <|endoftext|> beginning of Book B...\n```\n\nThat is normal packed-language-model structure.\n\n \n## \nA simple tokenizer-aware packing implementation\n\nFor a small/medium corpus, I would start with an intentionally simple implementation before introducing a specialized packing framework.\n\nAssume the train/validation split has already been made at the source level.\n\n``` python\nfrom datasets import Dataset\nfrom transformers import AutoTokenizer\n\nMODEL_ID = \"Qwen/Qwen3-0.6B-Base\"\nBLOCK_SIZE = 2048\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\neod_id = tokenizer.convert_tokens_to_ids(\"<|endoftext|>\")\n\ndef pack_books(records, block_size=BLOCK_SIZE):\n    \"\"\"\n    Clear reference implementation, not a streaming-optimized one.\n\n    records:\n        iterable of {\"book_id\": ..., \"text\": ...}\n    \"\"\"\n\n    stream = []\n\n    for record in records:\n        ids = tokenizer(\n            record[\"text\"],\n            add_special_tokens=False,\n        )[\"input_ids\"]\n\n        stream.extend(ids)\n        stream.append(eod_id)\n\n    n_full_blocks = len(stream) // block_size\n    n_used = n_full_blocks * block_size\n\n    blocks = [\n        stream[i:i + block_size]\n        for i in range(0, n_used, block_size)\n    ]\n\n    dropped_tail_tokens = len(stream) - n_used\n\n    lm_dataset = Dataset.from_dict({\n        \"input_ids\": blocks,\n        \"labels\": [block.copy() for block in blocks],\n    })\n\n    return lm_dataset, dropped_tail_tokens\n```\n\nThis gives you:\n\n- exact tokenizer-aware blocks;\n- no character-count approximation;\n- one EOD after each book;\n- no overlapping targets;\n- no padding for full blocks;\n- explicit accounting of the final incomplete tail.\n\nFor large corpora I would implement the same idea incrementally/streamingly rather than constructing one giant Python list, but the data semantics can remain the same.\n\nThe final incomplete block has several possible policies:\n\n### \n\nSimple and often fine if it is a tiny fraction of the corpus.\n\nBut count it:\n\n```\nprint(\"Dropped tail tokens:\", dropped_tail_tokens)\n```\n\n### \n\nUseful if you implement batched/streaming preprocessing and do not want to lose a remainder at every processing batch.\n\n### \n\nAlso valid. In that case, create an `attention_mask` and set labels to `-100` **only for actual padding positions**.\n\nFor example, conceptually:\n\n```\nlabels = input_ids.copy()\n\nfor i, is_real_token in enumerate(attention_mask):\n    if not is_real_token:\n        labels[i] = -100\n```\n\nFor completely full, equal-length blocks, precomputing `labels = input_ids.copy()` and using a simple/default collator is particularly easy.\n\nThis is also close to the core idea in Hugging Face’s [`run_clm.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py), which concatenates tokenized examples, splits them into `block_size` chunks, and copies `input_ids` into `labels`.\n\nOne small difference worth knowing: the stock example drops the incomplete remainder of each batched `group_texts()` operation. The source currently notes that `map(batched=True)` normally processes groups of 1,000 texts. This is not necessarily a serious loss, but for a relatively small novel corpus I would simply count the discarded tokens rather than assuming it is negligible.\n\n \n## \nShould the chunks overlap?\n\nFor ordinary continued pretraining where every token in every block contributes to the causal-LM loss, I would start with:\n\n```\nblock 0: tokens 0..2047\nblock 1: tokens 2048..4095\nblock 2: tokens 4096..6143\n...\n```\n\nrather than:\n\n```\nblock 0: tokens 0..2047\nblock 1: tokens 1792..3839\n...\n```\n\nWith the second scheme, the overlap is not merely extra context: if all labels are active, those repeated tokens become training targets multiple times. That changes their effective weighting.\n\nThat may occasionally be intentional, but I would not make it the default.\n\nThere is another valid design:\n\n```\n[overlapping prefix used only as context][new target tokens]\n```\n\nwhere labels for the repeated prefix are masked.\n\nHugging Face’s [fixed-length perplexity guide](https://huggingface.co/docs/transformers/main/perplexity) uses exactly this distinction for sliding-window evaluation: overlapping tokens can be supplied as context while the already-accounted-for targets are set to `-100`.\n\nThat does not mean you need sliding-window training here. It just illustrates why:\n\n“using overlap for context”\n\nand\n\n“training twice on every overlapped token”\n\nare different designs.\n\nFor a first CPT pipeline, consecutive non-overlapping blocks are much easier to reason about.\n\n \n## \nShould fixed blocks respect chapter or scene boundaries?\n\nI would preserve chapter/scene structure in the **canonical corpus**, but I would not initially require every training sequence to end exactly at a chapter or scene boundary.\n\nSuppose a chapter is 5,300 tokens and your block size is 2,048.\n\nYou could simply get:\n\n```\nblock 1: chapter tokens 0..2047\nblock 2: chapter tokens 2048..4095\nblock 3: chapter tokens 4096..5299 + following text...\n```\n\nThat is a normal fixed-block causal-LM representation.\n\nTrying to force every chapter into its own training sequence can instead produce:\n\n- truncation;\n- substantial padding;\n- highly variable sequence lengths;\n- inefficient packing.\n\nThere *is* legitimate research motivation for preserving document integrity more carefully. For example, [Fewer Truncations Improve Language Modeling](https://proceedings.mlr.press/v235/ding24f.html) studies ordinary concatenate-and-split pretraining and proposes Best-fit Packing to reduce unnecessary document truncation while retaining efficient packing.\n\nI would treat that as an **advanced comparison**, not as evidence that every Korean-novel chapter must be kept intact.\n\nSo my order would be:\n\n1. preserve all semantic boundaries in your canonical data;\n2. establish a simple fixed-block baseline;\n3. only then compare document-aware packing if preserving complete chapters/scenes seems important to your actual evaluation goal.\n\nThis also leaves the door open to more specialized long-context training later without making the initial dataset pipeline depend on it.\n\n \n## \nTrain/validation splitting: split according to what you want validation to mean\n\nI would avoid randomly generating thousands of adjacent 2048-token blocks first and *then* assigning those blocks independently to train and validation.\n\nOtherwise neighboring excerpts from the same novel can easily occur on both sides, making validation less independent than it appears.\n\nThe correct source boundary depends on what you want to measure.\n\n### \n\nHold out complete books first:\n\n```\nbooks\n  ├─ train books\n  └─ validation books\n\nthen tokenize/pack each split independently\n```\n\nThis is conceptually similar to long-form language-model datasets such as [PG-19](https://github.com/google-deepmind/pg19), where complete books are assigned to train, validation, and test partitions.\n\nThere is also broader language-model evidence that train/evaluation overlap can distort evaluation; see [Deduplicating Training Data Makes Language Models Better](https://aclanthology.org/2022.acl-long.577/). I would not infer from that that your corpus necessarily has a duplication problem—it is simply a good reason not to create avoidable adjacent-text leakage yourself.\n\n### \n\nHolding out an entire book may remove too much training data.\n\nThen a reasonable compromise can be:\n\n- complete held-out chapters; or\n- one or more contiguous held-out spans.\n\nJust interpret that validation correctly: it measures held-out text **within the same source/domain**, not generalization to a completely unseen book.\n\n### \n\nA within-book validation split can still be useful for loss monitoring / early stopping. It just answers a narrower question.\n\nSo I would choose the split unit from the evaluation goal, rather than treating “book split” as a universal rule.\n\n \n## \nA small Qwen/Transformers padding caveat\n\nThere is one implementation detail I would check once if you use generic dynamic padding.\n\nIn the current [`Qwen3-0.6B-Base` tokenizer configuration](https://huggingface.co/Qwen/Qwen3-0.6B-Base/blob/main/tokenizer_config.json), `<|endoftext|>` is exposed as both the `eos_token` and `pad_token`.\n\nMeanwhile, the current implementation of [`DataCollatorForLanguageModeling(mlm=False)`](https://github.com/huggingface/transformers/blob/main/src/transformers/data/data_collator.py) constructs labels and sets positions whose token ID equals `pad_token_id` to `-100`.\n\nThat combination matters if `<|endoftext|>` is also an intentional **real EOD inside the sequence**: value-based masking cannot distinguish\n\n```\nreal <|endoftext|> document boundary\n```\n\nfrom\n\n```\n<|endoftext|> used as padding\n```\n\njust from the token ID.\n\nThis general failure mode has also been discussed in [Transformers issue #23530](https://github.com/huggingface/transformers/issues/23530).\n\nSo if your pipeline uses that kind of collator, I would inspect one batch and verify:\n\n``` php\nreal EOD position        -> label is still the EOD token ID\nactual padding position  -> label is -100\n```\n\nIf you produce completely full fixed-length blocks yourself, the easiest route is to precompute:\n\n```\nlabels = input_ids.copy()\n```\n\nand use a simple collator that only stacks the equal-size examples.\n\nIf you keep padded tails, mask padding based on the **actual attention/padding mask**, rather than blindly masking every occurrence of the EOD token ID.\n\nThis is a small implementation detail, not a reason to change the overall dataset design.\n\n \n## \nWhat block size should you actually use?\n\nI do not think there is a generally established answer such as:\n\nKorean novels should use exactly 2048 tokens.\n\n`1024`, `2048`, `4096`, etc. are engineering/training choices.\n\nFor your goal, I would first measure the corpus with the exact tokenizer:\n\n```\nlengths = [\n    len(tokenizer(text, add_special_tokens=False)[\"input_ids\"])\n    for text in ds[\"text\"]\n]\n\nprint(\"min:\", min(lengths))\nprint(\"max:\", max(lengths))\nprint(\"mean:\", sum(lengths) / len(lengths))\n```\n\nThen choose a block length based on:\n\n- available VRAM / training throughput;\n- how much local narrative context you want per update;\n- total corpus size;\n- whether longer sequences materially reduce batch size;\n- what you intend to evaluate.\n\nStarting with 1024 or 2048 is perfectly reasonable as a simple baseline; 4096 is also a natural comparison if the training setup handles it comfortably. I would not claim one is intrinsically best without actually comparing the resulting training/evaluation behavior.\n\nAlso, do not choose the training length only from one tokenizer metadata number.\n\nAt the moment, the HF files for `Qwen/Qwen3-0.6B-Base` expose:\n\nThose fields are useful configuration information, but neither says that 131,072—or even 32,768—is the sensible sequence length for your particular continued-pretraining run.\n\n“Model can represent a long context” and “this is the most useful/economical training block size for this corpus” are different questions.\n\n \n## \nAbout the 20,000+ character rows\n\nI would not add an arbitrary character-count limit just to keep Dataset rows short.\n\nA Dataset row can hold an entire book while the training preprocessing later produces many fixed-token examples from it.\n\nThis pattern is not unusual in long-form language modeling. For example, [PG-19](https://github.com/google-deepmind/pg19) is explicitly organized around complete books as documents.\n\nSo:\n\n```\nDataset row:\n    150,000 characters of a book\n```\n\ndoes **not** imply:\n\n```\nmodel forward pass:\n    all 150,000 characters at once\n```\n\nThe latter is controlled by the tokenized training blocks.\n\nFor ordinary novels, I would therefore monitor:\n\n```\nnumber of tokens\nnumber of resulting blocks\nremainder/padding\n```\n\nrather than enforcing a fixed maximum number of characters in the canonical Dataset.\n\nExtremely large individual files can eventually become a preprocessing-memory/streaming engineering issue, but that is separate from the model’s sequence-length limit.\n\n \n## \nIf by “Hugging Face Dataset” you also mean publishing the text on the Hub\n\nCreating a local `datasets.Dataset` and publishing the underlying novels to the Hub are separate decisions.\n\nAll of the preprocessing above can be done locally.\n\nIf you later want to upload the actual novel text to a Dataset repository, check the source/license/redistribution rights first. Hugging Face supports both public and private dataset repositories and provides Dataset-card metadata for documenting things such as license and provenance.\n\nSee [Sharing a dataset to the Hub](https://huggingface.co/docs/datasets/main/upload_dataset).\n\nThat does not affect the technical choice between book/chapter rows and token blocks; it is simply a separate distribution/provenance question.\n\n \nSo, if I wanted the least complicated **known-good first version**, I would probably do this:\n\n```\nKeep original TXT files unchanged\n        ↓\nRepair only verified artificial hard wraps\n        ↓\nStore one book per row\n(or chapter rows + book_id if that is more convenient)\n        ↓\nChoose train/validation source units before token chunking\n        ↓\nTokenize raw prose with the exact Qwen tokenizer\n        ↓\nAdd <|endoftext|> at the chosen independent-document boundary\n(probably each book initially)\n        ↓\nPack into consecutive fixed-length blocks\n(1024 or 2048 is a reasonable first experiment)\n        ↓\nNo overlap initially\n        ↓\nlabels = input_ids for real tokens\n        ↓\nInspect a few decoded blocks, EOD positions, labels, and dropped/padded tail tokens\n        ↓\nTrain\n```\n\nThen, only if the baseline gives you a reason to do so, I would separately experiment with:\n\n```\nlonger block sizes\ndocument-aware packing\nchapter/scene-preserving packing\ncontext-only overlap\ncross-document attention isolation\n```\n\nThat way you do not have to solve every long-context or packing question before you can build a good Dataset.\n\nThe main thing I would preserve carefully is the **clean canonical corpus and its book/chapter metadata**. Once that exists, generating a different 1024/2048/4096-token training representation later is cheap; reconstructing lost paragraph/chapter/book structure from already-chunked training rows is much harder.", "url": "https://wpnews.pro/news/best-way-to-convert-long-korean-novel-txt-files-into-a-hugging-face-dataset-for", "canonical_source": "https://discuss.huggingface.co/t/best-way-to-convert-long-korean-novel-txt-files-into-a-hugging-face-dataset-for-causal-lm-fine-tuning/179933#post_5", "published_at": "2026-09-06 11:01:01+00:00", "updated_at": "2026-09-07 02:03:17.251493+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "developer-tools"], "entities": ["Hugging Face", "Qwen"], "alternates": {"html": "https://wpnews.pro/news/best-way-to-convert-long-korean-novel-txt-files-into-a-hugging-face-dataset-for", "markdown": "https://wpnews.pro/news/best-way-to-convert-long-korean-novel-txt-files-into-a-hugging-face-dataset-for.md", "text": "https://wpnews.pro/news/best-way-to-convert-long-korean-novel-txt-files-into-a-hugging-face-dataset-for.txt", "jsonld": "https://wpnews.pro/news/best-way-to-convert-long-korean-novel-txt-files-into-a-hugging-face-dataset-for.jsonld"}}