YouTube comment sentiment (3-class)
DistilRoBERTa encoder (top layers fine-tuned) + custom head, CLS pooling. Labels: Negative (0), Neutral (1), Positive (2). Max length 128.
This is a custom nn.Module, not a stock AutoModelForSequenceClassification.
Load it with model.py from this repo:
import json, torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import AutoTokenizer
from model import YTmodel
m = YTmodel(pretrained=False)
m.load_state_dict(load_file(hf_hub_download("Avinash159-159/yt-comment-sentiment", "model.safetensors")))
m.eval()
Live demo / API: https://huggingface.co/spaces/Avinash159-159/yt-comment-sentiment-demo
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