corpus-statistics 0.1.0

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Corpus Statistics: A Very Basic (for now) spaCy Pipeline Component

If you want to know what tokens your pipeline has seen, this is the component for you.

pip install git+https://github.com/pmbaumgartner/corpus_statistics

⚡️ Example

from spacy.lang.en import English

# Use some example data
from datasets import load_dataset
dataset = load_dataset("imdb")
texts = dataset["train"]["text"]

# ✨ start the magic 
nlp = English()  # or spacy.load('a_model')
nlp.add_pipe("simple_corpus_stats")

for doc in nlp.pipe(texts):
    # ➡️ do your pipeline stuff! ➡️
    pass

corpus_stats = nlp.get_pipe("simple_corpus_stats")

# check if a token has been processed through this pipeline
token = "apple"
if token in corpus_stats:
    token_count = corpus_stats[token]
    print(f"'{token}' mentioned {token_count} times")

# 'apple' mentioned 24 times

It's got all your favorite legomena like hapax and dis.

only_seen_once = len(corpus_stats.hapax_legomena)
percent_of_vocab = only_seen_once / corpus_stats.vocab_size
print(f"{percent_of_vocab*100:.1f}% tokens only occurred once.")
# 47.6% tokens only occurred once.

only_seen_twice = len(corpus_stats.dis_legomena)
percent_of_vocab_2x = only_seen_twice / corpus_stats.vocab_size
print(f"{percent_of_vocab_2x*100:.1f}% tokens occurred twice.")
# 12.3% tokens occurred twice.

We counted some things too:

# corpus_stats.vocabulary is a collections.Counter 🔢
print(*corpus_stats.vocabulary.most_common(5), sep="\n")
# ('the', 289838)
# (',', 275296)
# ('.', 236702)
# ('and', 156484)
# ('a', 156282)

mean_doc_length = sum(corpus_stats.doc_lengths) / corpus_stats.corpus_length
print(f"Mean doc length: {mean_doc_length:.1f}")
# Mean doc length: 272.5

Use in Model Training and Config Files

This can be quite helpful if you wanted to know what tokens were seen in your training data. You can include this component in your training config as follows.

...
[nlp]
lang = "en"
pipeline = ["simple_corpus_stats", ...]
...

[components]

[components.simple_corpus_stats]
factory = "simple_corpus_stats"
n_train = 1000  # This is important! See below

⚠️ 🔁 If you use this component in a training config, your pipeline will see the same docs multiple times, due to the number of training epochs and evaluation steps, so the vocab counter will be incorrect. To correct for this, you need to specify the number of examples in your training dataset as the n_train config parameter.

import spacy

nlp = spacy.load("your_trained_model")
corpus_stats = nlp.get_pipe("simple_corpus_stats")

assert min(corpus_stats.vocabulary.values()) == 1

# value from config
assert len(corpus_stats.doc_lengths) == 1000