vizwiz val annotations

source /home/coolhand/html/datavis/data_trove/cache/vizwiz_val_annotations.json 4,319 rows 5 columns profiled 2026-05-01 raw JSON static .html .ipynb Report Notebook

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dataset summary · high confidence anthropic:claude-opus-4-7

This dataset contains 4,319 rows from the VizWiz validation annotations, structured around image filenames, the questions asked about each image, the answers, an answer_type label, and an answerable flag. The questions column is the most interesting: about 35% are duplicates, with 'What is this?' alone appearing 523 times, suggesting a heavy concentration of generic identification queries. Answer_type is dominated by 'other' (62%) and 'unanswerable' (32%), and the answerable flag confirms that roughly 32% of items are flagged as not answerable — a key signal for any downstream modeling. The image column is uniquely identifying per row and not worth deeper analysis, while the answers column was skipped by the profiler.

citing: row_count · columns.question.stats.duplicate_rate · columns.question.top_values · columns.answer_type.top_values · columns.answer_type.stats.top_rate · columns.answerable.stats.mean · columns.answerable.stats.zero_rate · columns.question.language_counts

Charts the summary said to look at first

answer_type · Shows the strong skew toward 'other' and 'unanswerable' categories that together cover ~94% of rows.

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Top values for answer_type (4 unique shown, of 4 total).
value	count	share
other	2691	62.3%
unanswerable	1385	32.1%
yes/no	195	4.5%
number	48	1.1%

answerable · Highlights that about 32% of questions are flagged as not answerable.

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Histogram bins for answerable (median: 1.0).
bin	count
0 – 0.025	1385
0.025 – 0.05	0
0.05 – 0.075	0
0.075 – 0.1	0
0.1 – 0.125	0
0.125 – 0.15	0
0.15 – 0.175	0
0.175 – 0.2	0
0.2 – 0.225	0
0.225 – 0.25	0
0.25 – 0.275	0
0.275 – 0.3	0
0.3 – 0.325	0
0.325 – 0.35	0
0.35 – 0.375	0
0.375 – 0.4	0
0.4 – 0.425	0
0.425 – 0.45	0
0.45 – 0.475	0
0.475 – 0.5	0
0.5 – 0.525	0
0.525 – 0.55	0
0.55 – 0.575	0
0.575 – 0.6	0
0.6 – 0.625	0
0.625 – 0.65	0
0.65 – 0.675	0
0.675 – 0.7	0
0.7 – 0.725	0
0.725 – 0.75	0
0.75 – 0.775	0
0.775 – 0.8	0
0.8 – 0.825	0
0.825 – 0.85	0
0.85 – 0.875	0
0.875 – 0.9	0
0.9 – 0.925	0
0.925 – 0.95	0
0.95 – 0.975	0
0.975 – 1	2934

question · Top question values reveal how often generic prompts like 'What is this?' dominate the dataset.

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Character-length distribution for question (mean: 35.10141236397314).
chars	count
7 – 13	759
13 – 20	550
20 – 26	931
26 – 33	609
33 – 39	368
39 – 46	250
46 – 52	143
52 – 58	143
58 – 65	96
65 – 71	84
71 – 78	68
78 – 84	42
84 – 91	35
91 – 97	35
97 – 103	30
103 – 110	23
110 – 116	19
116 – 123	12
123 – 129	23
129 – 136	14
136 – 142	8
142 – 148	14
148 – 155	8
155 – 161	6
161 – 168	10
168 – 174	8
174 – 180	8
180 – 187	3
187 – 193	5
193 – 200	3
200 – 206	3
206 – 213	2
213 – 219	1
219 – 225	2
225 – 232	1
232 – 238	1
238 – 245	0
245 – 251	1
251 – 258	0
258 – 264	1

question · Question length distribution shows most are short (median 26 chars) with a long tail up to 264 chars.

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Character-length distribution for question (mean: 35.10141236397314).
chars	count
7 – 13	759
13 – 20	550
20 – 26	931
26 – 33	609
33 – 39	368
39 – 46	250
46 – 52	143
52 – 58	143
58 – 65	96
65 – 71	84
71 – 78	68
78 – 84	42
84 – 91	35
91 – 97	35
97 – 103	30
103 – 110	23
110 – 116	19
116 – 123	12
123 – 129	23
129 – 136	14
136 – 142	8
142 – 148	14
148 – 155	8
155 – 161	6
161 – 168	10
168 – 174	8
174 – 180	8
180 – 187	3
187 – 193	5
193 – 200	3
200 – 206	3
206 – 213	2
213 – 219	1
219 – 225	2
225 – 232	1
232 – 238	1
238 – 245	0
245 – 251	1
251 – 258	0
258 – 264	1

Schema

5 columns

Per-column summary. Click column name to jump to its detail.
				Alerts
image	text	0.0%	4,319	near_unique one_word
question	text	0.0%	2,798	multilingual duplicates
answers	unknown	0.0%	—	skipped
answer_type	categorical	0.0%	4
answerable	numeric	0.0%	2

image

text identifier near_unique one_word

This column holds image filenames following the pattern `vizwiz_val_########.jpg`, with all 4319 values being unique single tokens of exactly 23 characters. There are no nulls, duplicates, or vocabulary variation — every row maps one-to-one to a distinct image in what appears to be the VizWiz validation split. The negative Flesch score is an artifact of scoring filenames as prose and can be ignored. Treatment: Use as a foreign key to load the corresponding image file; do not feed as text to a model. high · anthropic:claude-opus-4-7

n: 4,319
nulls: 0 (0.0%)
unique: 4,319
len_min: 23
len_max: 23
len_mean: 23
len_median: 23
len_p95: 23
word_mean: 1
word_median: 1
n_empty: 0
n_duplicates: 0
duplicate_rate: 0
vocab_size: 4,319
readability_flesch_mean: -47.98
emoji_rate: 0
url_rate: 0
one_word_rate: 1
allcaps_rate: 0
boilerplate_rate: 0

question

text free_text multilingual duplicates

Short natural-language questions, mostly English (4308/4319) and overwhelmingly identification prompts — "What is this?" alone appears 523 times and the top 10 values are all generic "what is/color/says" queries. Heavy duplication (35.2%, 1521 rows) and a small vocab (2779 unique words across 4319 rows) suggest a VQA-style prompt set rather than diverse free text. Mean length is 35 chars / 7.3 words with very high Flesch readability (101.7), and a handful of non-English rows (es, la, it, fy, hu, ia, ast) introduce minor language drift. Treatment: Tokenize and embed for modelling; deduplicate or weight by frequency given the 35% duplicate rate. high · anthropic:claude-opus-4-7

n: 4,319
nulls: 0 (0.0%)
unique: 2,798
len_min: 7
len_max: 264
len_mean: 35.1
len_median: 26
len_p95: 95
word_mean: 7.259
word_median: 5
n_empty: 0
n_duplicates: 1,521
duplicate_rate: 0.3522
vocab_size: 2,779
readability_flesch_mean: 101.7
emoji_rate: 0
url_rate: 0
one_word_rate: 0
allcaps_rate: 0.002547
boilerplate_rate: 0.003473

answers

unknown other skipped

The column 'answers' was skipped by the profiler, so its kind is unknown and no descriptive statistics were computed. All 4319 rows are non-null, but uniqueness, type, and value distribution are unavailable. The name suggests it holds response content, likely structured (e.g., nested objects or arrays) which is why automatic profiling bailed out. Treatment: Inspect raw values manually and parse into a typed structure before further profiling. low · anthropic:claude-opus-4-7

n: 4,319
nulls: 0 (0.0%)
unique: —

answer_type

categorical label

Categorical label with only 4 distinct values across 4319 rows and no nulls, classifying answers into 'other', 'unanswerable', 'yes/no', and 'number'. The distribution is heavily imbalanced: 'other' covers 62.3% and 'unanswerable' another 1385 rows, while 'number' appears only 48 times. Entropy ratio of 0.61 confirms the skew toward the top two classes. Treatment: One-hot or integer-encode; consider class-weighting or stratified sampling given the imbalance toward 'other'. high · anthropic:claude-opus-4-7

n: 4,319
nulls: 0 (0.0%)
unique: 4
top_value: other
top_rate: 0.6231
cardinality: 4
entropy: 1.225
entropy_ratio: 0.6127

answerable

numeric label

Binary 0/1 flag indicating whether a question is answerable, with 4319 rows and no nulls. Roughly 68% are marked answerable (mean 0.6793) and 32% are zeros, giving a moderate class imbalance toward the positive class. Only two unique values confirm this is a clean indicator rather than a probability score. Treatment: Use directly as a binary target; account for the ~68/32 class imbalance during training or evaluation. high · anthropic:claude-opus-4-7

n: 4,319
nulls: 0 (0.0%)
unique: 2
min: 0
max: 1
mean: 0.6793
median: 1
std: 0.4668
q1: 0
q3: 1
iqr: 1
skew: -0.7684
kurtosis: -1.41
n_outliers: 0
outlier_rate: 0
zero_rate: 0.3207