---
language:
- en
library_name: transformers
pipeline_tag: token-classification
tags:
- token-classification
- named-entity-recognition
- ner
- bert
- tinybert
- healthcare
- medical
widget:
- text: book an appointment with Dr. Patel next Monday at 3pm
- text: I've had a severe headache since yesterday morning
- text: remind me to take 10mg of my blood pressure pill every day at 8am
- text: invite my brother John at john@example.com to my care circle
---

# Bert_NER_Ausa

A lightweight **token-classification (NER)** model that extracts structured entities from short health-assistant utterances — appointments, symptoms, allergies, routines/medication, and profile details. It is the entity-extraction stage of the AUSA Hub voice/text assistant, paired with the SetFit intent router [`aadiausa/Set_Fit_Ausa`](https://huggingface.co/aadiausa/Set_Fit_Ausa).

## Model details

- **Architecture:** `BertForTokenClassification` (TinyBERT — 4 hidden layers, hidden size 312, 12 attention heads, ~14.5M parameters)
- **Tokenizer:** WordPiece, uncased (`bert-base-uncased` vocab, 30522 tokens)
- **Max sequence length:** 512 tokens
- **Tagging scheme:** BIO — 55 labels = `O` + B-/I- for 27 entity types

### Entity types (27)

| Domain | Entities |
|---|---|
| Symptoms & allergies | `ALLERGY`, `SYMPTOM`, `SEVERITY`, `ONSET`, `DOSAGE` |
| Scheduling | `PROVIDER`, `DATE`, `START_TIME`, `END_TIME`, `ROUTINE`, `FREQUENCY`, `SCHEDULED_TIME`, `START_DATE`, `END_DATE`, `INTERVAL`, `DURATION`, `DAY_OF_WEEK` |
| Profile / contacts | `FULL_NAME`, `EMAIL`, `PHONE`, `ADDRESS`, `GENDER`, `HEIGHT`, `WEIGHT`, `RELATION`, `INVITE_METHOD`, `PERMISSION` |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

model_id = "aadiausa/Bert_NER_Ausa"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

ner = pipeline("token-classification", model=model, tokenizer=tok,
               aggregation_strategy="simple")

ner("book an appointment with Dr. Patel next Monday at 3pm")
# -> [{'entity_group': 'PROVIDER', 'word': 'dr. patel', ...},
#     {'entity_group': 'DATE', 'word': 'next monday', ...},
#     {'entity_group': 'START_TIME', 'word': '3pm', ...}]
```

## Intended use & limitations

- Designed for **short, first-person health-assistant commands** in English. Performance on long-form clinical notes or other domains is not guaranteed.
- The model detects spans that may correspond to personal data (`FULL_NAME`, `EMAIL`, `PHONE`, `ADDRESS`); it does **not** validate, store, or de-identify them — downstream handling is the integrator's responsibility.
- Not a medical device and not intended for diagnosis or treatment decisions.

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