---
language:
  - en
license: apache-2.0
tags:
  - question-classification
  - text-classification
  - onnx
  - english
  - eat
  - calibrated
datasets:
  - TigreGotico/EAT
---

# eat-classifiers

English question answer-type (EAT) classifiers trained on the
[TigreGotico/EAT](https://huggingface.co/datasets/TigreGotico/EAT) dataset
(30,017 questions, 53 fine-grained labels across 7 TREC categories).

Two-stage inference (eat7 gates eat53) achieves **93.4% macro F1** on the test set.

Used by [little_questions](https://github.com/OpenJarbas/little_questions).

## Label taxonomy

7 main categories, 53 sub-types:

| Main | Sub-types |
|------|-----------|
| `ABBR` | abb, exp |
| `BOOL` | yesno |
| `DESC` | def, desc, manner, reason |
| `ENTY` | animal, body, color, cremat, currency, dismed, event, food, instru, lang, letter, other, plant, product, religion, sport, substance, symbol, techmeth, termeq, veh, word |
| `HUM` | desc, gr, ind, title |
| `LOC` | city, country, landmass, mount, other, state, water |
| `NUM` | code, count, date, dist, money, ord, other, perc, period, speed, temp, volsize, weight |

## Models

| File | Input variant | Output[1] |
|------|---------------|-----------|
| `eat53_logreg_EN_0.9.0.onnx` | punctuated (written) | decision score |
| `eat53_sgd_EN_0.9.0.onnx` | punctuated (written) | decision score |
| `eat53_svm_EN_0.9.0.onnx` | punctuated (written) | decision score |
| `eat53_svm_cal_EN_0.9.0.onnx` | punctuated (written) | calibrated probability |
| `eat53_svm_cal_unpunct_EN_0.9.0.onnx` | unpunctuated (ASR/voice) | calibrated probability |
| `eat7_logreg_EN_0.9.0.onnx` | punctuated (written) | decision score |
| `eat7_sgd_EN_0.9.0.onnx` | punctuated (written) | decision score |
| `eat7_svm_EN_0.9.0.onnx` | punctuated (written) | decision score |
| `eat7_svm_cal_EN_0.9.0.onnx` | punctuated (written) | calibrated probability |
| `eat7_svm_cal_unpunct_EN_0.9.0.onnx` | unpunctuated (ASR/voice) | calibrated probability |

Both punctuated and unpunctuated variants are provided.
Use the unpunctuated (`_unpunct`) model for ASR / voice assistant input.

## Two-stage inference

```python
import onnxruntime as rt, numpy as np, json

sess7  = rt.InferenceSession("eat7_svm_cal_EN_0.9.0.onnx")
sess53 = rt.InferenceSession("eat53_svm_cal_EN_0.9.0.onnx")
classes7  = json.loads(sess7.get_modelmeta().custom_metadata_map["classes"])
classes53 = json.loads(sess53.get_modelmeta().custom_metadata_map["classes"])
main_of_53 = [c.split(":")[0] for c in classes53]

def classify(text):
    inp = np.array([text], dtype=object)
    main = classes7[int(sess7.run(None, {"input": inp})[0][0])]
    _, probs = sess53.run(None, {"input": inp})
    row = probs[0].copy()
    for j, m in enumerate(main_of_53):
        if m != main:
            row[j] = 0.0
    row /= row.sum()
    return classes53[int(np.argmax(row))], float(row.max())

print(classify("Who invented the telephone?"))  # ('HUM:ind', 0.96)
```

## Benchmarks

Full results: [BENCHMARKS.md](BENCHMARKS.md)