xaitalk

pretty relevant

Explain any model
with one line of code

See what your model actually learned — not what you hoped it learned. 40+ methods across PyTorch, TensorFlow, and JAX. Open source.

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Works withPyTorchTensorFlowJAX
40+XAI Methods
3Frameworks
17Architectures
r ≥ 0.95Cross-framework

See what AI sees

Real attribution results from xaitalk validation runs.

DistilBERT · gradient_x_input · PyTorch

Thismoviewasabsolutelyfantastic!Ilovedeveryminuteofit.
Low
HighAttribution

Same API.
Different
frameworks.

xaitalk detects your framework automatically. The unified API stays the same — the native implementation underneath changes per framework.

Switch framework to see what runs under the hood.

Unified API
import torch
import xaitalk

result = xaitalk.explain(model, x, method="gradient", target_class=0)
Native implementation
# What happens internally:
model.eval()
x = x.detach().clone().requires_grad_(True)
output = model(x)
target = torch.zeros_like(output)
target[0, 0] = 1.0
output.backward(gradient=target)
attribution = x.grad.detach().cpu().numpy()

XAI belongs in production,
not just in papers

Explainability has been a research topic for years. xaitalk makes it a practical tool — so you can actually verify, debug, and trust your AI before it reaches users.

Shadow models for closed-source APIs

Train an open model to replicate a closed-source API on your specific workflow — email classification, content moderation, credit scoring. Then explain every prediction. Replace black-box costs with transparent decisions.

xaitalk.explain(shadow_model, email, method="lrp_epsilon")shadow modelscost reductiontransparency

Verify learning during training

Run attributions at checkpoints to see what your model focuses on at each epoch. Catch Clever Hans moments early — is it learning the feature or the artifact? Compare methods to build confidence before deployment.

xaitalk.compare_methods(model_v3, x, ["gradient", "lrp_epsilon"])training loopClever Hans detectionepoch comparison

Fairness with counterfactual testing

Would the credit decision change if the applicant's gender were different? xaitalk's counterfactual fairness method flips protected attributes and compares attributions — concrete evidence for auditors and regulators.

xaitalk.explain(model, x, method="fairness_counterfactual")counterfactualbias detectionGDPREU AI Act

Give stakeholders real instruments

Doctors reviewing AI-flagged scans, loan officers explaining rejections, compliance teams auditing automated decisions — xaitalk turns model outputs into visual evidence that non-technical people can evaluate.

result = xaitalk.explain(model, x, method="integrated_gradients")human-in-the-loopnon-technical usersaudit trail

Any input dimension. Any architecture.

CNNs, Transformers, LLMs, GNNs, RNNs, diffusion models, protein folders, game engines.

0D

Tabular

Credit scoring, features

1D

Sequences

ECG, audio, text

2D

Images

X-rays, photos, scans

3D

Video

Surveillance, sports

4D

Spatiotemporal

Chess, V-JEPA

Start explaining your models

Open source library. Cloud API. Integration services.

Open Source

pip install xaitalk[all]

View on GitHub

Cloud API

GPU-powered inference

API Documentation