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abr-6024
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Model Details
Model Provider
abr-6024
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
v1 was trained on 15 cities of synthetic geography and failed on real-style addresses (about 58% on a stress set I wrote by hand). v3 is trained on addresses whose locality, pincode, district and state come from the real India Post pincode directory (156k localities, Tamil Nadu over-sampled to 44%), so the model learns real pincode / district / state relationships. The previous version stays in this repo's git history.
city = the district name (or the metro name for big cities), e.g. a village in Karur district gets
"city": "Karur". If your application wants the town the customer wrote, post-process or use another model.area = locality / village / post-office name. flat = the door / house / plot number only.Held-out synthetic test (800 addresses). Exact match after lower-casing and removing . and ,.
| Slice | Full record match |
|---|---|
| All (n=800) | 94.4% |
| Locality drawn from the training pool (n=300; most were still never trained on) | 95.0% |
| Localities never seen (n=300) | 94.7% |
| Whole districts never seen (n=200: Ariyalur, Tenkasi, Udupi, Wayanad, Satara, Mathura) | 93.0% |
| Field | Accuracy |
|---|---|
| flat | 100.0% |
| building | 99.9% |
| street | 99.8% |
| landmark | 99.5% |
| area | 96.1% |
| city | 97.0% |
| state | 98.5% |
| pincode | 100% |
| phone | 100% |
| valid JSON | 100% |
| Set | Result |
|---|---|
| 30 Tamil Nadu addresses | 29 / 30 (96.7%); the miss: 15 Gandhi Salai, Tirupur 641601 → flat left empty |
| 36 mixed-India addresses written before v2 (city = the town written) | 80.6% (city 88.9%) |
Caveats. The Tamil Nadu set was written by the model author after designing the generator, using the
same conventions and vocabulary, so it shows the anticipated patterns work; it is not an independent
benchmark. Scores come from the published weights (one training run; a second run of the same recipe
differed by a few tenths of a point). The 36-address set uses the town as city, which differs from v3's district convention, so part
of that gap is the convention. Neither set contains real customer addresses.
python
import json, torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "abr-6024/indian-address-parser-1.5B"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.float16, device_map="auto") SYSTEM = ("You are an Indian address parser. Convert the address into JSON with exactly these keys: " "flat, building, street, landmark, area, city, state, pincode, phone. " "Use an empty string for anything missing. Output only JSON.")msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "Flat 3B, Sri Murugan Apartments, Peelamedu, Coimbatore 641004"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=200, do_sample=False)print(json.loads(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)))Use the exact system prompt above; the model was trained with it. eval.py in this repo reproduces the scores.
Python (3.9+; the first call downloads the 1 GB model once, then it works offline):
text
pip install india-address-parserpython
from india_address_parser import parseparse("D.No 14/2, Gandhi Road, Pollachi, Coimbatore - 642001")Node.js (18+; needs Ollama running, or use the HTTP API below):
text
npm install india-address-parsernpx india-address-parser --pull # downloads the model into Ollama, oncenpx india-address-parser "Flat 6, Kaveri Residency, Gandhipuram, Coimbatore 641012"text
pip install "india-address-parser[server]"ADDRESS_API_KEY=choose-a-secret india-address-parser serve --port 8000curl -X POST http://localhost:8000/parse -H "Authorization: Bearer choose-a-secret" -H "Content-Type: application/json" -d '{"address": "D.No 14/2, Gandhi Road, Pollachi, Coimbatore - 642001"}'Endpoints: /parse, /parse_batch (up to 100), /health. A Dockerfile is in the
GitHub repo. From Node:
parseAddress(address, { apiUrl: "http://localhost:8000", apiKey: "choose-a-secret" }). About 3 seconds per address on a
laptop CPU, so it suits emails, imports and confirmation steps better than blocking a checkout. You host it yourself;
addresses are personal data, so set an API key before exposing it.
Ollama directly: ollama pull hf.co/abr-6024/indian-address-parser-1.5B-GGUF:Q4_K_M
| GGUF files (Q4_K_M 1 GB, Q8_0 1.7 GB) | abr-6024/indian-address-parser-1.5B-GGUF |
| Package pages | PyPI, npm |
| Code, generator, training scripts | github.com/ab-rar-6024/indian-address-parser |
| Example outputs | showcase page |
The quantized files scored the same as these weights within noise on my test sets (details on the GGUF card).
25 Mount Road) are a known weak spot: in my hand-written check the model dropped the
number from 15 Gandhi Salai, Tirupur 641601. Building names without a common suffix are also not guaranteed.Contains information from the All India Pincode Directory, Department of Posts, Government of India,
published on data.gov.in and listed under the Government Open Data License – India (GODL-India). The
directory was obtained through a public GitHub mirror (dropdevrahul/pincodes-india); check the current
terms at data.gov.in before reusing the data.
Model weights: Apache-2.0, inherited from Qwen2.5-1.5B-Instruct. Credit to the Qwen team for the base model.
import json, torchfrom transformers import AutoModelForCausalLM, AutoTokenizer repo = "abr-6024/indian-address-parser-1.5B"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, dtype=torch.float16, device_map="auto") SYSTEM = ("You are an Indian address parser. Convert the address into JSON with exactly these keys: " "flat, building, street, landmark, area, city, state, pincode, phone. " "Use an empty string for anything missing. Output only JSON.")msgs = [{"role": "system", "content": SYSTEM}, {"role": "user", "content": "Flat 3B, Sri Murugan Apartments, Peelamedu, Coimbatore 641004"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(ids, max_new_tokens=200, do_sample=False)print(json.loads(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)))from india_address_parser import parseparse("D.No 14/2, Gandhi Road, Pollachi, Coimbatore - 642001")npm install india-address-parsernpx india-address-parser --pull # downloads the model into Ollama, oncenpx india-address-parser "Flat 6, Kaveri Residency, Gandhipuram, Coimbatore 641012"pip install "india-address-parser[server]"ADDRESS_API_KEY=choose-a-secret india-address-parser serve --port 8000curl -X POST http://localhost:8000/parse -H "Authorization: Bearer choose-a-secret" -H "Content-Type: application/json" -d '{"address": "D.No 14/2, Gandhi Road, Pollachi, Coimbatore - 642001"}'