WaterSheep

Website · Demo · Code

WaterSheep answers yes/no, single-choice, rating and multi-label questions about any text, with a probability for every option. Version 0.1.0 (watersheep-20260928-125452).

Usage

pip install transformers torch

from transformers import pipeline

ws = pipeline(model="samratduttaofficial/WaterSheep", trust_remote_code=True)
ws("I was charged twice.", question="Which team should handle this?", options=["billing", "shipping", "support"])

Type	Options	Answer
noul	none (yes/no)	probability of yes
choice	any labels	the best option
score	a digit scale, e.g. 1 to 5	the expected level
multi	any labels, with type="multi"	every option above the threshold

Every answer includes a probability for each option.

Download

hf download samratduttaofficial/WaterSheep --local-dir WaterSheep

Or with Git (requires Git LFS):

git clone https://huggingface.co/samratduttaofficial/WaterSheep

Then load it from the folder, offline:

ws = pipeline(model="WaterSheep", trust_remote_code=True)

API

Deploy it as an Inference Endpoint, then:

curl https://YOUR-ENDPOINT -H "Authorization: Bearer $HF_TOKEN" -H "Content-Type: application/json" -d '{"inputs": "I was charged twice.", "parameters": {"question": "Which team should handle this?", "options": ["billing", "shipping", "support"]}}'

JavaScript

No install; runs in the browser:

<script type="module">
  import { decide } from "https://samratduttaofficial.github.io/WaterSheep/watersheep.js";
  console.log(await decide("I was charged twice.", "Which team should handle this?", ["billing", "shipping", "support"]));
</script>

With a downloaded copy on your web server, call load({ base: "WaterSheep/" }) first.

Other languages: run onnx/model_quantized.onnx with ONNX Runtime; watersheep.js shows the input format.

Evaluation

Evaluation	Accuracy	ECE
In-distribution test split	77.8%	0.026
Held-out datasets, not seen in training	61.2%	0.043

ECE is the expected calibration error (lower is better).

Accuracy against confidence for each question type, before (raw) and after calibration.

Benchmarks

Benchmark	Suite	Questions	Accuracy	ECE	In training data
goemotions	sentiment	2000	22.4%	0.023	other split
hatecheck	safety	2000	75.1%	0.139	no
legal_abercrombie	legal	95	21.1%	0.316	no
legal_contract_nli_confidentiality_of_agreement	legal	82	69.5%	0.177	no
legal_corporate_lobbying	legal	490	68.4%	0.216	no
legal_cuad_audit_rights	legal	1216	86.3%	0.041	no
legal_definition_classification	legal	1337	56.9%	0.279	no
legal_function_of_decision_section	legal	367	24.3%	0.245	no
legal_hearsay	legal	94	56.4%	0.307	no
legal_overruling	legal	2000	62.5%	0.151	no
legal_personal_jurisdiction	legal	50	50.0%	0.160	no
legal_privacy_policy_qa	legal	2000	58.9%	0.274	no
legal_proa	legal	95	51.6%	0.379	no
legal_ucc_v_common_law	legal	94	62.8%	0.171	no
prompt_injection	safety	116	91.4%	0.079	other split
xstest	safety	450	73.6%	0.140	no

Training

Training loss and learning rate (left); validation accuracy by question type (right).

Share of synthetic examples kept after verification, by question type (left) and by family (right).

Limitations

• English only.
• Long inputs are truncated.
• Rating-scale answers are less accurate than the other types.
• Probabilities are calibrated on data like the training data; validate them on your own.
• Not for high-stakes decisions (medical, legal, financial, hiring) on its own.

License

Apache 2.0 (LICENSE). Trained on openly licensed data; credits in NOTICE.

Citation

@misc{watersheep,
  author = {Samrat Dutta},
  title  = {WaterSheep: calibrated decisions for any text},
  year   = {2026},
  url    = {https://huggingface.co/samratduttaofficial/WaterSheep}
}

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