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geraldadli/
Kratt

Name: Kratt
Availability: InStock

FacebookAI/xlm-roberta-base

finetune

mit

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Kratt Comment Authenticity Classifier

Fine-tuned xlm-roberta-base that scores a YouTube comment as bot or authentic. Built for Kratt, a media-literacy tool that helps viewers read comment sections more critically — it flags likely bot activity, it never deletes or hides anything.

⚠️ Required input format — read this first

This model was not trained on raw comment text. Every input was built as:

niche <niche_tag> . <likes bucket> . <replies bucket> . <cleaned comment text>

Concrete example:

niche low effort . few likes . no replies . first!!

Piece	Values	Rule
`niche_tag`	`genuine`, `copycat`, `low effort`	property of the video, hyphens replaced with spaces
likes bucket	`few likes` (<2), `some likes` (2–9), `many likes` (≥10)	from `like_count`
replies bucket	`no replies` (0), `has replies` (>0)	from `reply_count`
comment text	lowercased except ALL-CAPS tokens (shouting is a signal); URLs/@mentions/#hashtags replaced with `<URL>` / `<USER>` / `<TAG>`; emoji kept as-is	see Preprocessing below

If you skip this prefix and feed raw comment text, the model will perform far worse than the reported metrics — the niche and engagement context are load-bearing, not optional metadata. This was a deliberate design choice: niche_tag is a video-level property that determines a large part of the label, so the model cannot infer it from the comment text alone.

The three placeholder tokens <URL>, <USER>, <TAG> are registered as tokenizer special tokens — load the tokenizer from this repo, not a fresh xlm-roberta-base tokenizer, or these will be split into sub-word garbage.

How to use

python

import re
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

REPO = "geraldadli/Kratt"  # replace with your HF repo id
tokenizer = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForSequenceClassification.from_pretrained(REPO)

INVISIBLE_RE = re.compile('[​‎‏⁠⁡⁢⁣⁤﻿­]')
URL_RE  = re.compile(r'(?:https?://|www\.)\S+', re.IGNORECASE)
USER_RE = re.compile(r'@[\w.\-]+')
TAG_RE  = re.compile(r'#\w+')

def clean_text(text):
    s = INVISIBLE_RE.sub('', str(text))
    s = URL_RE.sub(' <URL> ', s)
    s = USER_RE.sub(' <USER> ', s)
    s = TAG_RE.sub(' <TAG> ', s)
    return ' '.join(t if (len(t) >= 2 and t.isupper()) else t.lower() for t in s.split())

def like_phrase(n):  return 'many likes' if n >= 10 else ('some likes' if n >= 2 else 'few likes')
def reply_phrase(n): return 'has replies' if n > 0 else 'no replies'

def build_input(text, niche_tag, like_count, reply_count):
    return (f"niche {niche_tag.replace('-', ' ')} . {like_phrase(like_count)} . "
            f"{reply_phrase(reply_count)} . {clean_text(text)}")

model_input = build_input(
    text="first!!", niche_tag="low-effort", like_count=0, reply_count=0)

inputs = tokenizer(model_input, truncation=True, max_length=128, return_tensors="pt")
with torch.no_grad():
    probs = torch.softmax(model(**inputs).logits, dim=-1)[0]

print({model.config.id2label[i]: round(p.item(), 3) for i, p in enumerate(probs)})

Labels

id	label
0	`bot`
1	`authentic`

Training data

~[FILL IN: total rows after filtering] YouTube comments, per-comment-tagged by a local LLM (Qwen2.5-7B-Instruct) into genuine / copycat / low-effort / ads_spam, combined with each comment's video-level niche_tag (genuine / copycat / low-effort).

Labels are weak/derived, not human-annotated. A (niche_tag, comment_tag) → authenticity-score matrix converts the combination into a binary label (authentic if score ≥ 0.5) and a per-sample training weight (|score − 0.5| × 2). The core idea: the same comment type means different things in different niches — e.g. a low-effort comment under a genuine-niche video (tutorial, stunt) is usually just a casual human (high authenticity), while the same comment type under a low-effort video (fast-consume clips) matches an observed bot pattern (low authenticity). Ambiguous combinations (e.g. copycat in a genuine niche, score 0.5) get a training weight near zero and barely influence the model.

ads_spam-tagged comments were excluded from training — spam detection is handled by a separate rule engine in the product, not this classifier.

Training procedure

Base model: xlm-roberta-base (multilingual, cased — casing matters for the ALL-CAPS signal)
3 epochs, batch size 16 (grad. accumulation ×2), LR 2e-5, max sequence length 128, fp16
Grouped split by video_id (no video's comments appear in both train and test) with a per-video cap of 500 comments so one large video can't dominate a niche's data or degenerate the split, plus a guard requiring ≥2 videos and 15–25% share in the test set
Weighted loss: per-sample weight from the authenticity matrix (above) × class weight, where class weights are computed on the effective weighted mass per class (not raw row counts) so the sample weights and class balance don't fight each other in the loss

Evaluation

Test set: grouped by video, 8581 comments across 18 videos.

              precision    recall  f1-score   support

         bot       0.72      0.79      0.75       991
   authentic       0.77      0.70      0.74      1009

    accuracy                           0.75      2000
   macro avg       0.75      0.75      0.75      2000
weighted avg       0.75      0.75      0.75      2000

Positive class for the confusion matrix is bot; a false positive means an authentic comment flagged as bot — the costlier error for a media-literacy tool, since it wrongly casts doubt on a real person.

Limitations

Weak labels: ground truth comes from an LLM's per-comment judgment + a hand-picked authenticity matrix, not human annotation. Treat metrics as agreement with that scheme, not absolute truth — a hand-checked sample (handcheck_sample.csv in the training notebook) is the real validation step.
English-only: non-English comments were filtered out of training via language detection; behavior on other languages is untested.
Requires the exact input format above — this is not a general-purpose comment classifier.
Not intended to auto-moderate or delete content — designed to surface a bot-likelihood signal for human review, in line with the project's media-literacy (not censorship) goal.

Intended use

Input to Kratt's /analyze pipeline: score each fetched comment, aggregate into a bot-likelihood percentage and evidence breakdown shown to the end user.