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intfloat/multilingual-e5-small with a classification head to score Bluesky posts by feed worthiness.1import torch
2import torch.nn.functional as F
3from transformers import AutoTokenizer, AutoModelForSequenceClassification
4
5model_id = "Circularmachines/atproto_classifier"
6tokenizer = AutoTokenizer.from_pretrained(model_id)
7model = AutoModelForSequenceClassification.from_pretrained(model_id)
8model.eval()
9
10texts = ["passage: some post text here"]
11inputs = tokenizer(texts, return_tensors="pt", truncation=True, padding=True, max_length=512)
12
13with torch.no_grad():
14 probs = F.softmax(model(**inputs).logits, dim=-1)
15
16score = probs[0][1].item() # P(feed-worthy)
17label = int(score > 0.5)intfloat/multilingual-e5-smallBertForSequenceClassification (2 classes: not feed-worthy / feed-worthy)passage: {text} (matches e5 training convention)