README.md

---
license: apache-2.0
language:
- multilingual
tags:
- cross-encoder
- reranker
- feed-ranking
- enterprise-feed
- learning-to-rank
pipeline_tag: text-classification
base_model: nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large
library_name: transformers
---

# Third-Pass Feed Ranker (job-title × feed-post relevance)

A lightweight **cross-encoder** that scores how relevant an enterprise social-feed post is to a
viewer, given only the viewer's **job title** and the **post text**. It is a *third-pass reranker*:
it re-scores a small candidate slate (~20 items) from earlier passes to surface a genuinely
job-relevant post that was buried below the top slot.

- **Input:** `job_title` (query) + `post_text` (passage) → single relevance score (higher = more relevant)
- **Base:** `nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large` — a **generic** distilled
  multilingual MiniLM-L12 (~117M params), *not* a search reranker. Fine-tuning defines relevance
  purely from this task's data, without a general-search "dense-technical-text = relevant" prior.
- **Runtime:** server-class **CPU** at scale (~120 pairs/sec on 8 CPU threads; INT8/ONNX 3–6× more).
- **Trained with a listwise ranking objective** (optimizes which item wins the slate), pointwise
  inference unchanged.

## What it does — and what it deliberately avoids
- **Ranks by MEANING, not keywords.** Relevance is decided by substance (e.g. "patient-monitoring
  escalation protocol" → a clinical role). Masking role words barely changes the ranking, and the
  role keyword is trained to be uncorrelated with the label (neither a shortcut nor a penalty).
- **Not fooled by dense technical jargon.** Training injects dense-technical posts as hard negatives
  for non-technical roles, so a machine-learning or software post does **not** score as broadly
  relevant to, say, a nurse or a chef — it only wins for roles it actually fits.

## Usage — scoring + the gate
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
name = "FDS-Iterations/third-pass-feed-ranker"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name).eval()

def scores(title, posts):
    enc = tok([title]*len(posts), posts, truncation=True, max_length=160, padding=True, return_tensors="pt")
    with torch.no_grad():
        return model(**enc).logits.squeeze(-1).tolist()

TAU = 1.0    # promotion margin; calibrate per deployment (see below)
def third_pass(title, slate):
    s = scores(title, slate)
    challenger = max(range(1, len(slate)), key=lambda i: s[i])
    return ("promote", challenger) if s[challenger] - s[0] > TAU else ("no_change", None)
```
**The model only scores.** The gate (which item to move to slot 1, and the τ threshold) is *your*
logic — none of it is in the weights. **τ is distribution-sensitive**: on realistic feeds a value
around **0.5–1.5** trades recovery vs. false-promotion; re-calibrate on a sample of your own
no-relevance feeds. Higher τ is more conservative.

## Evaluation
Measured on an **external hold-out of 157 job titles that never appear in training** (adjacent
real-world variants of trained roles + high-volume roles the training taxonomy under-covers), each
with substance gems buried in 20-item feeds:

| metric | value |
|---|---|
| relevant post surfaced to #1 in its feed (novel titles) | **~60%** |
| role's gem beats a dense-technical distractor | **~85%** |
| realistic-density recovery (buried gem promoted) | **~0.64** @ low τ |
| false-promotion on no-relevance feeds | **~0.3%** |

On a deliberately **adversarial worst-case slate** (~9 simultaneous strong competitors, including
posts relevant to *other* roles), exact-#1 recovery drops to ~0.3 — a stress bound, not a
realistic-feed number.

## Intended use & limitations
- Re-ranking short enterprise-feed candidate slates by job-title relevance; abstains on role-less
  titles.
- **Trained entirely on SYNTHETIC data** (LLM-generated posts + synthetic slates). **Validate on
  your own data before production.**
- **Fine role-discrimination is limited.** It reliably separates a relevant post from ordinary
  filler, but distinguishing a role's exact post from a *closely adjacent* role's post is near the
  capacity ceiling of a 117M model.
- **Out-of-distribution phrasing is the weak axis.** Terse status fragments, log/ticket snippets,
  and atypical wording score more noisily than well-formed posts — for all roles.
- **Very high-volume roles absent from the training taxonomy** (a few common titles) generalize at
  reduced magnitude; adding them to training stabilizes them.
- Cross-lingual mixing (non-English title vs English-only feed) is weaker than same-language feeds.
- Relevance is title-driven; recency/importance beyond relevance must live in your decision logic.

## Training & method
Job titles derived from a public occupation taxonomy; LLM-generated posts where relevance is by
**substance** and the role is never named; adversarial 20-item slates with held-out roles and posts,
in both well-formed and terse registers. Objective = pointwise relevance MSE **plus a listwise
ranking loss** that pushes the relevant post to rank #1 within its slate. Two anti-bias measures:
the role keyword is **rebalanced** to be label-uncorrelated, and **dense-technical hard negatives**
are injected into non-technical roles' slates so technical vocabulary is not a global relevance
signal. Headline metrics are measured on titles **never seen in training**.

## License & attribution
Apache-2.0. Inherits from `nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large` — verify its
license carries through. Training posts were generated with a Qwen model; review the applicable terms.