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4latte/
techpto-classifier

fasttext-distillation

mit

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this is a classification model that sorts tweets/profiles off the probability that it is tech/crypto related. this was a model created for a job that fell short. this is a tf-idf model, distilled from a transformer model that I also made. maybe ill upload that soon

Techpto Classifier

This repository contains a lightweight production classifier for detecting whether X/Twitter posts and profiles are crypto-related, tech-related, both, or neither.

Files

text_classifier.json: Rust-compatible hashed logistic-regression classifier.
model_config.json: labels, expected inputs, and recommended thresholds.
distill_metrics.json: proxy evaluation metrics from distillation.
recommended_thresholds_distillation.json: thresholds tuned against the V7 fastText teacher.
full_run_manifest.json: counts and thresholds from the large full-corpus run.

Recommended Thresholds

The high-precision full-corpus run used:

json

{
  "post_crypto": 0.85,
  "post_tech": 0.90,
  "profile_crypto": 0.90,
  "profile_tech": 0.99
}

The original distillation-tuned thresholds were:

json

{
  "post_crypto": 0.58,
  "post_tech": 0.44,
  "profile_crypto": 0.34,
  "profile_tech": 0.38
}

Full-Corpus Run

Using the high-precision thresholds:

Posts scanned: 928,484,069
Post matches: 7,728,133
Profiles scanned: 2,667,815,773
Profile matches: 7,915,096

One corrupt post shard was skipped and is listed in full_run_manifest.json.

Notes

This is not a standard Transformers checkpoint. It is a compact hashed-feature linear classifier intended for very high-throughput local scanning. Metrics in distill_metrics.json are proxy metrics against teacher/weak labels rather than a final human-labeled benchmark.

4latte/techpto-classifier