---
license: apache-2.0
language: en
library_name: transformers
base_model: distilbert-base-uncased
tags:
  - text-classification
  - trading
  - intent-classification
  - distilbert
  - lora
  - onnx
  - english
pipeline_tag: text-classification
---

# distilbert-trade-decision-classifier-v1

DistilBERT fine-tuned with LoRA r=32 for classifying user replies to trading-agent proposals into one of six decision intents. Pairs with a regex fast-path and a confirmation prompt for the bookends of a reply-routing pipeline.

## How it works

Trading agents that DM proposals ("Approve / decline / hold / size N / trim N?") get free-form text replies back. This model converts the reply into one of six discrete intents so the agent can route it deterministically.

The model is invoked AFTER a fast-path regex tries the canonical phrases first ("approve", "decline", "size 10"). The regex handles routine replies; the model handles everything the regex doesn't match.

```
Reply text in
   ↓
Canonical-phrase regex      ← catches structured replies cheaply
   ↓ (no match)
THIS MODEL                  ← classifies into 6 intent labels
   ↓
Decision rule:
   • confidence ≥ 0.85 AND label ≠ UNCLEAR → commit
   • else                                  → confirmation prompt to the user
```

## Labels (6)

| Label          | What it covers                                                          |
| -------------- | ----------------------------------------------------------------------- |
| APPROVE        | Execute the proposal as stated. "approve", "yes", "let's go", "send it" |
| DECLINE        | Kill the proposal. "no", "pass", "kill it", "hard pass"                 |
| HOLD           | Active deferral — user is engaged but not deciding yet. "hold off", "checking", "let me think", "leaning approve" |
| COUNTER_SIZE   | Execute but at a different share count. "size 10", "dump half", "trim 50" |
| COUNTER_PRICE  | Execute but at a different limit price. "at $49", "limit 50", "trim at $48" |
| UNCLEAR        | Cannot safely commit. Multi-intent, ambiguous, off-topic, or sarcastic. Falls through to confirmation prompt. |

UNCLEAR is a trained refusal label, not a fallback. The model is expected to emit it on multi-intent, ambiguous, or off-topic inputs. Treat it as the model saying "I don't know, ask the human."

## Inputs

A single string with structural context tags prepended:

```
[dm|group][reply_to:N|no_reply_to][in_flight:K] <reply text>
```

- `[dm]` vs `[group]` — chat surface (DM vs group chat)
- `[reply_to:N]` vs `[no_reply_to]` — whether the user quote-replied to a specific proposal
- `[in_flight:K]` — number of proposals currently awaiting decision

Example inputs:
```
[dm][reply_to:200][in_flight:1] approve
[dm][no_reply_to][in_flight:1] dump half
[dm][reply_to:200][in_flight:2] trim at $49
```

The tags carry context the model can't infer from the text alone — "yes" with 1 proposal in flight is APPROVE; "yes" with 3 in flight and no quote-reply is structurally ambiguous and trained as UNCLEAR.

## Usage

### Python (transformers)

```python
from transformers import pipeline

clf = pipeline(
    "text-classification",
    model="DoDataThings/distilbert-trade-decision-classifier-v1",
)
result = clf("[dm][reply_to:200][in_flight:1] dump half")
print(result)
# [{'label': 'COUNTER_SIZE', 'score': 0.991}]
```

### Python (onnxruntime, CPU)

```python
import onnxruntime as ort
import numpy as np
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("DoDataThings/distilbert-trade-decision-classifier-v1")
sess = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])

text = "[dm][no_reply_to][in_flight:1] hold off"
enc = tok(text, truncation=True, max_length=64, return_tensors="np")
logits = sess.run(
    None,
    {"input_ids": enc["input_ids"], "attention_mask": enc["attention_mask"]},
)[0][0]
probs = np.exp(logits) / np.exp(logits).sum()
labels = ["APPROVE", "DECLINE", "HOLD", "COUNTER_SIZE", "COUNTER_PRICE", "UNCLEAR"]
print(labels[int(probs.argmax())], float(probs.max()))
# HOLD 0.943
```

## Deployment shape

The model is not safe to use standalone. Pair with:

- A confidence threshold (we recommend 0.85)
- Deterministic safety rails (position size, available cash, mode gate)
- A confirmation prompt for low-confidence cases

The model picks intent; the system decides whether to act. It does not have final authority over orders.

## Design decisions

**Narrow-waist split.** The model classifies INTENT only, not proposal context. By design, upstream code disambiguates which proposal the reply targets (via quote-reply or single-default rule), and the model only sees the locked-in case. This makes the model independent of ticker / setup / portfolio specifics — its job is interpreting "what did the user mean," not "which one."

**UNCLEAR as a trained refusal class.** A 5-label classifier forced to pick one of {APPROVE, DECLINE, HOLD, COUNTER_SIZE} on ambiguous input is dangerous. The 6th label is the model's escape valve — it's trained on multi-intent, ambiguous, off-topic, and sarcastic inputs so it can refuse rather than guess. Combined with the 0.85 confidence threshold, this caps the blast radius of misclassification: an unsafe input either yields UNCLEAR (refusal) or a non-UNCLEAR label with low confidence (falls through to confirmation prompt).

**Structural prefix as text, not special tokens.** The `[dm][reply_to:N][in_flight:K]` tags are concatenated into the input string and tokenized as regular subword pieces. This works with off-the-shelf DistilBERT — no special-token registration, no tokenizer config drift between train and serve. The model learns the bracket conventions naturally via attention.

**Six labels including COUNTER_PRICE.** Earlier versions used five labels. The sixth (COUNTER_PRICE) was added because "trim at $49 instead of $48" is a fundamentally different action from "size 10" — different downstream extraction (price vs share count). Conflating them would force the consumer to disambiguate post-classification, defeating the purpose of the intent label.

## Evaluation

Held-out eval set: 175 hand-curated adversarial examples, ~30 per class, zero-leakage verified against training.

| Label          | Precision | Recall | F1    | Count |
| -------------- | --------- | ------ | ----- | ----- |
| APPROVE        | 0.967     | 0.967  | 0.967 | 30    |
| DECLINE        | 1.000     | 0.933  | 0.966 | 30    |
| HOLD           | 0.970     | 0.941  | 0.955 | 34    |
| COUNTER_SIZE   | 0.968     | 1.000  | 0.984 | 30    |
| COUNTER_PRICE  | 1.000     | 1.000  | 1.000 | 25    |
| UNCLEAR        | 0.821     | 0.885  | 0.852 | 26    |
| **macro avg**  |           |        | **0.954** | 175 |
| **accuracy**   |           |        | **0.954** |     |

**Honest assessment.** Zero high-confidence misclassifications on eval (no row labeled wrong at confidence ≥ 0.85). DECLINE and COUNTER_PRICE both hit perfect precision (1.000). UNCLEAR is the weakest class at F1 0.85, and the HOLD/UNCLEAR boundary on multi-intent inputs ("approve but only half") is genuinely fuzzy — these cases can be reasonably labeled either way. The 0.85 confidence threshold is calibrated so weak cases fall to confirmation rather than commit wrong.

## Training

| Knob               | Value                                       |
| ------------------ | ------------------------------------------- |
| Base model         | distilbert-base-uncased                     |
| Adapter            | LoRA r=32 on attention projections (q_lin, v_lin) |
| Sequence length    | 64                                          |
| Batch size         | 32                                          |
| Learning rate      | 5e-5, cosine schedule, 10% warmup           |
| Epochs             | 3, early-stop on eval macro-F1              |
| Class weighting    | inverse-frequency (functionally uniform — data is balanced within 2%) |
| Hardware           | Single RTX 4090                             |
| Wall time          | ~9 seconds                                  |

## Limitations

1. Classifies INTENT only, not proposal context. The model never sees the actual proposal being responded to — upstream proposal-disambiguation must run before this model is invoked.
2. COUNTER_SIZE emits intent only; share count extraction is a separate downstream step (regex).
3. COUNTER_PRICE emits intent only; price extraction is a separate downstream step.
4. Trained on author-curated and synthetically-augmented data. Real-world reply variety may exceed training surface forms; expect ~5% of replies to fall to confirmation-prompt fallback.
5. UNCLEAR has the lowest F1 (0.85). The boundary with HOLD (active deferral vs no-position) is fuzzy on multi-intent inputs.
6. English-only. No localization in v1.

## Dataset

Training and evaluation data: [DoDataThings/trade-decision-classifier-v1-dataset](https://huggingface.co/datasets/DoDataThings/trade-decision-classifier-v1-dataset)

## License

Apache 2.0.