PyTorch语言模型训练：Google Ngram百万词库内容缺失问题问询

Hey there, let’s figure out why you’re not seeing words after "F" in your downloaded chunks—this is a common mix-up with the Google Ngram dataset versions.

The Core Issue: You Got the Frequency-Sorted Chunks, Not Alphabetical #

The 0-9 numbered chunks you downloaded are part of the top 1 million most frequent 1-grams, sorted by how often they appear (not alphabetically). Here’s the catch: high-frequency words are heavily biased toward early letters (A-F), so the least frequent words in each numbered chunk (which you’re checking with tail) might all fall in those early letter ranges. Later-letter words that are still in the top 1M are scattered across the chunks, just not at the tail of any single one.

How to Get All Top 1M Words (Including Post-F) #

If your goal is a dictionary of the 1M most common English words (sorted alphabetically), here’s what to do:

Option 1: Use Your Existing Chunks #

You already have all the data—you just need to combine and sort it properly:

• Merge all your 0-9 chunks into one file:

  cat googlebooks-eng-all-1gram-20120701-* > combined_top_1m.txt
  

• Extract just the word column (since each line follows the format word year count pages), remove duplicates (same word appears for multiple years), and sort alphabetically:

  awk '{print $1}' combined_top_1m.txt | sort | uniq > top_1m_words_sorted.txt
  

Now if you run tail top_1m_words_sorted.txt, you’ll see words from all letters (like "zoo", "zone", etc.—yes, they’re definitely in the top 1M!).

Option 2: Use the Full Alphabetical Dataset (For Absolute Accuracy) #

If you want to double-check you’re getting the true top 1M (in case the pre-sorted 1M dataset has any gaps), you can use the full alphabetical 1-gram dataset (split into a-z chunks):

• For each letter chunk, calculate the total frequency of each word by summing counts across all years.
• Collect all words with their total frequencies, sort by frequency in descending order, pick the top 1M entries, then sort alphabetically if needed.

Why tail on Individual Chunks Didn’t Work #

Each numbered chunk has words ordered by frequency (highest first), not alphabetical order. So the tail of chunk 9 shows the least frequent words in the entire top 1M, which still tend to be early letters. Combining all chunks first gives you every word in the top 1M, and sorting alphabetically lets you see the full range of letters.

内容的提问来源于stack exchange，提问作者Emanuele Giona