Update dataset card

README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+pretty_name: Speculative Decoding Papers
+size_categories:
+  - n<1K
+task_categories:
+  - text-classification
+  - text-generation
+tags:
+  - arxiv
+  - semantic-scholar
+  - papers
+  - research
+  - machine-learning
+configs:
+  - config_name: default
+    data_files: data.jsonl
+---
+
+# Speculative Decoding Papers — FineSet
+
+A research-paper dataset on **Speculative Decoding Papers**, assembled, deduplicated, and quality-scored by
+[FineSet](https://fineset.io) from arXiv and Semantic Scholar.
+
+> **📸 This is a dated snapshot — generated 2026-06-19.**
+> It is not auto-updated. Research on **Speculative Decoding Papers** moves fast — new papers land on arXiv every
+> week. Want this same dataset **refreshed daily**, on a topic *you* choose? See the bottom. ↓
+
+## Why this dataset
+
+- **Quality-scored:** `quality_score` float (0–1), blends citations with recency + code/venue signals — filter out the noise
+- **Papers with code:** 134 flagged via `has_code` — find reproducible work fast
+- **Deduplicated:** arXiv + Semantic Scholar cross-referenced, duplicate records merged
+- **Clean JSONL:** 485 records, one per line, normalized fields — no encoding garbage
+
+## Dataset details
+
+- **Records:** 485
+- **Date range:** 2022–2026
+- **Snapshot date:** 2026-06-19 (frozen — see note above)
+- **Sources:** arXiv, Semantic Scholar (cross-referenced, duplicates merged)
+- **arXiv categories:** cs.LG, cs.CL
+- **Quality scoring:** citations + recency + code/venue blend, 0–1 (p50=0.35, p90=0.61)
+- **Format:** JSONL, one record per line
+
+## Fields
+
+| Field | Type | Description |
+|---|---|---|
+| id | string | Deterministic SHA256 record id |
+| sources | list | Which sources contributed (`arxiv`, `semantic_scholar`) |
+| title | string | Paper title |
+| abstract | string | Full abstract |
+| authors | list | Author names |
+| categories | list | arXiv category codes |
+| fields_of_study | list | Semantic Scholar field tags |
+| published_date | string | ISO 8601 date |
+| url | string | arXiv abstract URL |
+| pdf_url | string\|null | Open-access PDF if available |
+| arxiv_id | string\|null | arXiv identifier |
+| doi | string\|null | DOI if available |
+| citation_count | int | Citation count (Semantic Scholar) |
+| influential_citation_count | int | Influential citations (Semantic Scholar) |
+| has_code | bool | Code repo detected in the arXiv comment |
+| code_url | string\|null | GitHub URL if detected |
+| venue | string\|null | Publication venue |
+| quality_score | float | 0–1, blended (citations + recency + code/venue) |
+
+## Quality score methodology
+
+`quality_score = max(impact, freshness)`, clamped to [0, 1], where:
+
+- **impact** = `max( log10(citations+1)/4 , log10(influential_citations+1)/2 )` —
+  realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
+- **freshness** = `recency × (0.35 + 0.30·has_code + 0.20·has_venue)` — a baseline
+  for recent papers (so a strong paper published this week isn't scored 0 just for
+  lacking citations), where `recency` is 1.0 for papers ≤60 days old and decays
+  linearly to 0 by ~18 months.
+
+Old highly-cited papers score on impact; brand-new papers score on freshness; old
+uncited papers score ~0. Useful for filtering training data by quality, not just age.
+
+## 👉 Want this on YOUR topic, updated daily?
+
+This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this
+**refreshed every day** on whatever topic you describe — new papers in, dedup and quality
+scoring automatic, export as JSONL/Parquet or push straight to the Hub.
+
+**Tell me the topic you'd want and I'll run the pipeline on it** — open a discussion on this
+dataset, it's free and it's how I decide what to build next.
+
+→ [fineset.io](https://fineset.io) — describe what you want to train on, get a dataset.
+Early-access waitlist open (referral skip available).