Upload folder using huggingface_hub

README.md

@@ -0,0 +1,104 @@
+# Data Cleaning Pipeline
+
+I filtered the [`mookiezi/Discord-Dialogues`](https://huggingface.co/datasets/mookiezi/Discord-Dialogues) dataset to obtain only high-quality english conversation examples for fine-tuning or other analytical tasks.
+
+The total data set went from **7,303,464** rows to **2,208** rows after strict filtering to remove the following:
+- Low conversation turns or short conversations
+- Non-English conversations
+- Duplicate conversations
+- Spammy/Repetitive conversations
+- Filler words
+
+# Rules
+## Phase 1 — Load, Parse & Size Filter
+
+The raw dataset, containing **7,303,464** samples, is loaded and each conversation's `text` field is parsed into structured `messages`. Message counts (`turns`) and token counts (`tokens`, via `cl100k_base`) are recomputed so later stages can reason about conversation size.
+
+Conversations are kept only if they meet **both** of the following:
+
+- At least **4 turns**
+- Between **128** and **2,048 tokens**
+
+Rows removed: **7,301,008** (7,303,464 → 2,456) — *the vast majority of the raw dataset was filtered out here for being too short, too long, or unparseable.*
+
+---
+
+## Phase 2 — Language Filter
+
+Each conversation's joined text is passed through a fast language detector. Only conversations detected as English with sufficient confidence are retained.
+
+A row is kept only when **both** hold:
+
+- Detected language is `en`
+- Detector confidence ≥ **0.80**
+
+Rows removed: **211** (2,456 → 2,245)
+
+---
+
+## Phase 3 — Near-Duplicate Removal (MinHash LSH)
+
+Exact and near-duplicate conversations are identified using character-5-gram MinHash signatures (256 permutations, bucketed into 32 bands of 8 rows). A row is dropped if it shares a band bucket with an already-kept row whose estimated Jaccard similarity is ≥ **0.70**. Exact duplicates are also caught via SHA-256 digest in the same pass. When duplicates exist, the **longest** copy is kept.
+
+Rows removed: **3** (2,245 → 2,242)
+
+---
+
+## Phase 4 — Semantic Near-Duplicate Removal
+
+Conversations that are paraphrases or rewordings of each other — but not necessarily string-similar — are removed using `all-MiniLM-L6-v2` embeddings and a 2048-bit random-hyperplane LSH (128 bands of 16 bits). A row is dropped when its exact cosine similarity to an already-kept row is ≥ **0.80**, gated by a Hamming pre-filter to cheaply prune unrelated candidates. As in Phase 3, the **longest** copy among duplicates is kept.
+
+Rows removed: **0** (2,242 → 2,242)
+
+---
+
+## Phase 5 — Filler & Spam Filter
+
+Conversations dominated by low-information or spammy content are removed, while noisy turns *inside* otherwise-good conversations are preserved. Each row is scored against the rules below, and any rule firing triggers a drop. Dropped rows are written to `dropped.jsonl` along with the triggering reasons.
+
+| Rule | Threshold | What it catches |
+|---|---|---|
+| `too_short` | total words < **20** | conversations too thin to be useful |
+| `high_filler` | filler words / total words ≥ **0.50** | small-talk / interjection chatter |
+| `low_unique` | distinct words / total words < **0.25** | repetitive spam |
+| `repetition` | most-frequent word / total words ≥ **0.55** | single-word spam |
+| `punct_spam` | non-alphanumeric chars / total chars ≥ **0.20** | `.....` spam |
+| `char_run` | chars inside 5+ char runs / total chars ≥ **0.20** | `PPPP...` / `EEEE...` spam |
+| `dup_messages` | repeated message contents / total messages ≥ **0.50** | repeated turns |
+| `single_word` | one-word turns / total turns ≥ **0.60** | `boop` / `blep` / `kk` spam |
+| `short_turns` | median turn length < **6 chars** | ultra-short `ok` / `hm` chatter |
+
+Rows removed: **34** (2,242 → 2,208)
+
+### Phase 5 drop-reason breakdown
+
+Of the 34 dropped rows (some triggered multiple rules):
+
+```
+5  char_run
+5  low_unique + repetition
+4  low_unique + dup_messages
+4  too_short
+4  too_short + char_run
+3  low_unique
+3  dup_messages
+3  punct_spam
+1  too_short + char_run + single_word
+1  single_word + short_turns
+1  dup_messages + single_word + short_turns
+```
+
+---
+
+## Cumulative Summary
+
+| Phase | Input | Output | Removed |
+|---|---|---|---|
+| 1 — Size filter | 7,303,464 | 2,456 | 7,301,008 |
+| 2 — Language | 2,456 | 2,245 | 211 |
+| 3 — Near-duplicate | 2,245 | 2,242 | 3 |
+| 4 — Semantic dedupe | 2,242 | 2,242 | 0 |
+| 5 — Filler / spam | 2,242 | 2,208 | 34 |
+
+**Total removed: 7,301,256 rows (~99.97% of the raw dataset).**
+**Final clean dataset: 2,208 high-quality conversations.**