README.md

Stats for stanfordnlp/sst2

Generated by dataset-stats.py over the train split.

• Total rows in split: 67,349
• Rows profiled: 5,000
• Columns: 3

Column overview

column	type	kind	null %	highlights
idx	Value(int32)	numeric	0.0%	min=0.00 · p50=2,499.50 · max=4,999.00 · distinct=5000
sentence	Value(string)	string	0.0%	distinct=4,999 · len p50=39 · max=255
label	ClassLabel	class_label	0.0%	positive=2758 · negative=2242

Per-column detail

idx

• type: Value(int32) • kind: numeric • null: 0 / 5,000 (0.0%)

{
  "kind": "numeric",
  "n": 5000,
  "min": 0.0,
  "p1": 49.99,
  "p25": 1249.75,
  "p50": 2499.5,
  "p75": 3749.25,
  "p99": 4949.01,
  "max": 4999.0,
  "mean": 2499.5,
  "std": 1443.3756441065507,
  "distinct": 5000
}

sentence

• type: Value(string) • kind: string • null: 0 / 5,000 (0.0%)

{
  "kind": "string",
  "n": 5000,
  "distinct": 4999,
  "len_min": 2,
  "len_p50": 39,
  "len_mean": 53.479,
  "len_p99": 194,
  "len_max": 255,
  "top_values": [
    {
      "value": "unfunny ",
      "count": 2
    },
    {
      "value": "hide new secretions from the parental units ",
      "count": 1
    },
    {
      "value": "contains no wit , only labored gags ",
      "count": 1
    },
    {
      "value": "that loves its characters and communicates something rather beautiful about human nature ",
      "count": 1
    },
    {
      "value": "remains utterly satisfied to remain the same throughout ",
      "count": 1
    },
    {
      "value": "on the worst revenge-of-the-nerds clichés the filmmakers could dredge up ",
      "count": 1
    },
    {
      "value": "that 's far too tragic to merit such superficial treatment ",
      "count": 1
    },
    {
      "value": "demonstrates that the director of such hollywood blockbusters as patriot games can still turn out a ",
      "count": 1
    },
    {
      "value": "of saucy ",
      "count": 1
    },
    {
      "value": "a depressed fifteen-year-old 's suicidal poetry ",
      "count": 1
    }
  ],
  "samples": {
    "shortest": "( ",
    "median": "piece , a model of menacing atmosphere ",
    "longest": "director george hickenlooper has had some success with documentaries , but here his sense of story and his juvenile camera movements smack of a film school undergrad , and his maudlin ending might not"
  }
}

label

• type: ClassLabel • kind: class_label • null: 0 / 5,000 (0.0%)

{
  "kind": "class_label",
  "n": 5000,
  "num_classes": 2,
  "value_counts": [
    {
      "id": 1,
      "name": "positive",
      "count": 2758,
      "pct": 55.16
    },
    {
      "id": 0,
      "name": "negative",
      "count": 2242,
      "pct": 44.84
    }
  ]
}

Reproduce

INPUT_DATASET=stanfordnlp/sst2 \
OUTPUT_DATASET=Tim-Pinecone/sst2-stats \
SPLIT=train LIMIT=5000 \
  bin/colab-hf-run recipes/dataset-stats.py

Machine-readable

Per-column rows live in the dataset's train split: one row per column with column, feature_type, kind, n_total, n_null, null_pct, and stats_json (full per-kind detail).