---
license: other
library_name: transformers
base_model: distilbert-base-uncased
tags:
  - text-classification
  - affect
  - emotion
  - distilbert
language:
  - en
---

# how-affect-v1 — Bridge-Grounded Affect Detector

A DistilBERT-based affect-valence classifier fine-tuned on **non-circular**
author-narrated affect labels (mined from public-domain novel narration via
BookNLP), rather than on LLM-generated personality scores.

## Why this exists

Production personality / emotion classifiers in companion AI are commonly trained
**on LLM labels** (e.g. Claude/GPT scores). Evaluation against those same LLM
labels is circular — the model only learns to imitate the labeling LLM. We
needed a HOW (affect) detector grounded in **independent human-written signal**
about how characters speak. Solution: harvest dialogue-tag adverbs + WordNet
emotion supersenses from BookNLP-processed novels (~1000 books, 25k labeled
quotes), bind them to the speaker's actual utterances, and train a probe.

## Metrics

Held-out test set (5,971 quotes, balanced neg/pos author affect):

| Model | Held-out AUC |
|---|---|
| Existing circular "emotion" dim (177-dim model trained on Claude scores) | 0.557 |
| Frozen-embedding probe (sentence-transformer + linear head) | 0.637 |
| **This model — DistilBERT end-to-end on bridge labels** | **0.678** |

Honest ceiling: ~0.68 is real but modest. Narrated affect ("said bitterly")
often lives in prosody, not lexical content, so text-only affect detection
has a structural ceiling. A voice/prosody channel is the path to higher AUC.

## Files

- `model.pt` — full state-dict: DistilBERT encoder + mean-pool + Linear(hidden→1) head.
- `metrics.json` — final held-out AUC + baseline comparison.

## Usage

The head is custom (DistilBERT + mean-pool + 1-logit), so you can't use
`AutoModelForSequenceClassification.from_pretrained` directly. Load like this:

```python
import torch
from transformers import AutoTokenizer, AutoModel

class AffectNet(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.enc = AutoModel.from_pretrained("distilbert-base-uncased")
        self.head = torch.nn.Linear(self.enc.config.hidden_size, 1)
    def forward(self, ids, mask):
        h = self.enc(input_ids=ids, attention_mask=mask).last_hidden_state
        m = mask.unsqueeze(-1).float()
        pooled = (h * m).sum(1) / m.sum(1).clamp(min=1e-6)
        return self.head(pooled).squeeze(1)

tok = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AffectNet()
model.load_state_dict(torch.load("model.pt", map_location="cpu"))
model.eval()

text = "I can't bear this any longer."
enc = tok(text, padding="max_length", truncation=True, max_length=48, return_tensors="pt")
with torch.no_grad():
    valence = torch.sigmoid(model(enc["input_ids"], enc["attention_mask"]))[0].item()
print(valence)  # ~1.0 = negative/distressed affect, ~0.0 = positive
```

## Training data (non-circular)

Bridge corpus from ~1000 BookNLP-processed novels (`corpus/booknlp_output/`):
for each character quote, the narration window (±7 tokens around the quote) was
scanned for emotion supersense spans (`verb.emotion`, `noun.feeling`) and
manner adverbs anchored to a speech verb ("said *bitterly*"). Quotes mapped
to net-negative vs net-positive author affect → 17,749 neg / 16,375 pos
balanced labels (29,852 total used, 23,881 train / 5,971 test).

## Architecture

- Base encoder: `distilbert-base-uncased` (~66M params).
- Head: `Dropout-free Linear(hidden_size, 1)` over mean-pooled token embeddings.
- Loss: `BCEWithLogitsLoss` on binary affect-valence.
- Trained 1-2 epochs on CPU (best epoch saved by held-out AUC; early-stopped when AUC stopped improving).
- Max input length: 48 tokens (quotes are short).

## License

Trained on derivatives of public-domain (Project Gutenberg) novels processed
via BookNLP. The model weights are released for research use; please consult
your jurisdiction's rules around derivative works for production deployment.