nli

Pure Erlang Natural Language Inference scoring backed by a multilingual ONNX model. Given a premise and a hypothesis, returns the probability that the premise entails the hypothesis.

Wraps tok (tokenization) and onyx (ONNX inference). No Python at runtime.

Installation

%% rebar.config
{deps, [{nli, "0.1.0"}]}.

Getting a model

pip install optimum
optimum-cli export onnx \
    --model MoritzLaurer/mDeBERTa-v3-base-mnli-xnli \
    ./model/

Any XNLI-compatible model with 3-class output (contradiction / neutral / entailment) works. nli auto-detects the input dtype (i32 vs i64) from the ONNX session.

Quick start

{ok, N}  = nli:load("path/to/tokenizer.json", "path/to/model.onnx"),

%% Entailment: high score
{ok, S1} = nli:score(N,
    <<"A man is eating food.">>,
    <<"A person is eating.">>),
%% S1 ~ 0.9

%% Contradiction: low score
{ok, S2} = nli:score(N,
    <<"A man is eating food.">>,
    <<"No one is eating.">>),
%% S2 ~ 0.02

nli:unload(N).

API

%% Load a tokenizer + ONNX NLI model.
-spec load(file:filename(), file:filename()) -> {ok, nli()} | {error, term()}.

%% Return the entailment probability for (Premise, Hypothesis). Range [0.0, 1.0].
-spec score(nli(), binary(), binary()) -> {ok, float()} | {error, term()}.

%% Release the ONNX session.
-spec unload(nli()) -> ok.

Notes

• Encoding: the pair is encoded as "Premise Hypothesis" (space-concatenated). This works correctly for DeBERTa-family models which do not use token_type_ids.
• Label order: XNLI models output [contradiction, neutral, entailment]; nli returns softmax(logits)[2].
• Supported models: any ONNX export of an XNLI-family multilingual NLI model (mDeBERTa, XLM-RoBERTa, etc.). fr/en/de/es/zh and more depending on the model.

License

Apache 2.0 — see LICENSE.