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CuriousDragon/ettin-17m-sponsor-v2
ettin-17m-sponsor-v2 is a token classification model from CuriousDragon. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.
Full retraining experiment on the audited dataset revision. It did not replace v1 and is kept for comparison only.
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From the Hugging Face model README
Full retraining experiment on the audited dataset revision. It did not replace v1 and is kept for comparison only.
jhu-clsp/ettin-encoder-17m @ 987607455c61e7a5bbc85f7758e0512ea6d0ae4cModernBertForTokenClassification)O, B-SPONSOR, I-SPONSOR, L-SPONSOR, U-SPONSORCuriousDragon/ettin-17m-sponsor-v2from transformers import AutoModelForTokenClassification, AutoTokenizer
repo = "CuriousDragon/ettin-17m-sponsor-v2"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForTokenClassification.from_pretrained(repo)
For full YouTube transcripts, tokenize into overlapping 768-token windows
(128-token overlap), decode BILOU spans, map characters back onto caption
cues, and merge gaps of 24 normalized characters or 1500 ms. See the Flow
ml/sponsor_detection inference pipeline.
| Split | Sponsor token P / R / F1 | Token accuracy |
|---|---|---|
| Validation | 0.937 / 0.892 / 0.914 | 0.974 |
| Test | 0.939 / 0.880 / 0.908 | 0.972 |
On-device or local inference over English video transcripts to propose paid sponsor ranges. In Flow, SponsorBlock remains authoritative when it has segments; these weights are a fallback / shadow detector.
Use v1 or v3-replay instead of this checkpoint for new work.
These weights are released under CC BY-NC-SA 4.0.
The base encoder jhu-clsp/ettin-encoder-17m
is MIT. Fine-tuning used SponsorBlock labels
(CC BY-NC-SA 4.0) and the pinned Xenova/sponsorblock-768
window corpus. Non-commercial use and share-alike apply to this derivative.
This project is not affiliated with SponsorBlock.