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da1ebc6 cc94070 da1ebc6 cc94070 da1ebc6 cc94070 da1ebc6 cc94070 da1ebc6 cc94070 da1ebc6 cc94070 da1ebc6 cc94070 3c6f6c7 cc94070 da1ebc6 cc94070 da1ebc6 cc94070 da1ebc6 cc94070 da1ebc6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | """Standalone loader for microblog-topic-classifier.
from modeling import load, render_post
clf = load(".") # or a local snapshot of the Hugging Face repo
doc = render_post(text="Chiefs clinch the AFC West", tags=["NFL"])
clf.classify([doc])[0]["paths"] # {"sports/american_football": 0.97, ...}
The model is a ModernBERT-base encoder with mean pooling and four linear heads:
broad topic (softmax), subtopic path (softmax), signals (independent sigmoids), and
tone (softmax). Broad and path probabilities use the temperatures in config.json.
"""
from __future__ import annotations
import json
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors.torch import load_file
from transformers import AutoConfig, AutoModel, AutoTokenizer
class TopicClassifier(nn.Module):
def __init__(self, encoder_config, n_broad: int, n_paths: int, n_signals: int, n_tones: int,
attn: str = "sdpa"):
super().__init__()
self.encoder = AutoModel.from_config(encoder_config, attn_implementation=attn)
h = self.encoder.config.hidden_size
self.drop = nn.Dropout(0.1)
self.broad = nn.Linear(h, n_broad)
self.path = nn.Linear(h, n_paths)
self.signals = nn.Linear(h, n_signals)
self.tone = nn.Linear(h, n_tones)
def forward(self, input_ids, attention_mask):
hs = self.encoder(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
m = attention_mask.unsqueeze(-1).to(hs.dtype)
pooled = self.drop((hs * m).sum(1) / m.sum(1).clamp(min=1)) # mean pooling
return self.broad(pooled), self.path(pooled), self.signals(pooled), self.tone(pooled)
class Classifier:
def __init__(self, model: TopicClassifier, tokenizer, cfg: dict, device: str):
self.model, self.tok, self.cfg, self.device = model, tokenizer, cfg, device
@torch.no_grad()
def classify(self, docs: list[str], batch_size: int = 64, top_broad: int = 5,
top_paths: int = 8) -> list[dict]:
"""Classify post documents (see render_post). Returns one dict per document with the
top broad topics and paths, and every signal and tone, as probabilities."""
c, out = self.cfg, []
for i in range(0, len(docs), batch_size):
enc = self.tok(docs[i:i + batch_size], padding=True, truncation=True,
max_length=c["max_len"], return_tensors="pt")
with torch.autocast(self.device, dtype=torch.bfloat16, enabled=self.device == "cuda"):
lb, lp, ls, lt = self.model(enc["input_ids"].to(self.device),
enc["attention_mask"].to(self.device))
pb = F.softmax(lb.float() / c["temperature_broad"], -1).cpu()
pp = F.softmax(lp.float() / c["temperature_path"], -1).cpu()
ps = torch.sigmoid(ls.float()).cpu()
pt = F.softmax(lt.float(), -1).cpu()
for j in range(pb.shape[0]):
bv, bi = pb[j].topk(min(top_broad, pb.shape[1]))
pv, pi = pp[j].topk(min(top_paths, pp.shape[1]))
out.append({
"broad": {c["broad"][k]: round(float(v), 4) for v, k in zip(bv, bi.tolist())},
"paths": {c["paths"][k]: round(float(v), 4) for v, k in zip(pv, pi.tolist())},
"signals": {n: round(float(ps[j][k]), 4) for k, n in enumerate(c["signals"])},
"tone": {n: round(float(pt[j][k]), 4) for k, n in enumerate(c["tones"])},
})
return out
def load(model_dir: str = ".", device: str | None = None) -> Classifier:
"""Load the classifier from a local directory (e.g. huggingface_hub.snapshot_download)."""
device = device or ("cuda" if torch.cuda.is_available() else "cpu")
with open(os.path.join(model_dir, "config.json")) as f:
cfg = json.load(f)
with open(os.path.join(model_dir, "encoder_config.json")) as f:
enc = json.load(f)
encoder_config = AutoConfig.for_model(**enc)
model = TopicClassifier(encoder_config, len(cfg["broad"]), len(cfg["paths"]),
len(cfg["signals"]), len(cfg["tones"]))
model.load_state_dict(load_file(os.path.join(model_dir, "model.safetensors")))
model.to(device).eval()
tok = AutoTokenizer.from_pretrained(model_dir)
return Classifier(model, tok, cfg, device)
# --- Post document (format "pd2") -------------------------------------------------------
_MAX_LINK_DESCRIPTION = 300
_MAX_QUOTE = 500
_MAX_IMAGE_TEXT = 300
def _one_line(s: str) -> str:
return " ".join(s.split())
def _truncate(s: str, n: int) -> str:
return s if len(s) <= n else s[:n - 1].rstrip() + "…"
def _media_summary(kinds: list[str]) -> str:
"""["image", "image", "video"] -> "2 images, 1 video" (kinds in order of first appearance)."""
counts: dict[str, int] = {}
for k in kinds:
k = _one_line(k)
if k:
counts[k] = counts.get(k, 0) + 1
return ", ".join(f"1 {k}" if n == 1 else f"{n} {k}s" for k, n in counts.items())
def render_post(text: str = "", tags: list[str] | None = None, alt_texts: list[str] | None = None,
link_domain: str = "", link_title: str = "", link_description: str = "",
quote: str = "", media: list[str] | None = None, labels: list[str] | None = None,
image_text: list[str] | None = None, image_descriptions: list[str] | None = None) -> str:
"""Render a post the way the model saw posts in training:
{text}
[tags] #tag1 #tag2
[media] 2 images, 1 video
[labels] porn, nudity
[alt] {alt 1} | {alt 2}
[image text] {words read from image 1} | {image 2}
[image description] {description 1} | {description 2}
[link] {domain} | {title} | {description}
[quote] {quoted post text}
- media: one entry per attachment, "image" or "video".
- labels: self-labels and moderation labels on the post or its author. Moderation actions
("!takedown", "!hide", ...) and "needs-review" are left out.
- image_text: words read from attachments that have no alt text (OCR).
- image_descriptions: short descriptions of attachments without alt text, e.g. from a
vision model, one or two sentences each.
Lines for empty fields are left out, as are empty parts of the link line. The post text
keeps its line breaks; everything else is collapsed to one line. Tags lose a leading '#'
and are deduplicated case-insensitively. Image text and descriptions are cut at 300
characters each, link descriptions at 300, quoted text at 500.
"""
lines = []
text = text.strip()
if text:
lines.append(text)
seen, clean_tags = set(), []
for t in tags or []:
t = _one_line(t).lstrip("#")
if t and t.lower() not in seen:
seen.add(t.lower())
clean_tags.append(t)
if clean_tags:
lines.append("[tags] " + " ".join("#" + t for t in clean_tags))
summary = _media_summary(media or [])
if summary:
lines.append("[media] " + summary)
clean_labels = []
for lab in labels or []:
lab = _one_line(lab)
if lab and not lab.startswith("!") and lab != "needs-review" and lab not in clean_labels:
clean_labels.append(lab)
if clean_labels:
lines.append("[labels] " + ", ".join(clean_labels))
alts = [a for a in (_one_line(a) for a in alt_texts or []) if a]
if alts:
lines.append("[alt] " + " | ".join(alts))
for prefix, items in (("[image text] ", image_text), ("[image description] ", image_descriptions)):
clean = [_truncate(s, _MAX_IMAGE_TEXT) for s in (_one_line(s) for s in items or []) if s]
if clean:
lines.append(prefix + " | ".join(clean))
link = [p for p in (_one_line(link_domain), _one_line(link_title),
_truncate(_one_line(link_description), _MAX_LINK_DESCRIPTION)) if p]
if link:
lines.append("[link] " + " | ".join(link))
quote = _truncate(_one_line(quote), _MAX_QUOTE)
if quote:
lines.append("[quote] " + quote)
return "\n".join(lines)
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