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# LIT Python API

## Design Overview

LIT is a modular system, comprising a collection of backend components (written in Python) and frontend modules (written in TypeScript). Most users will develop against the Python API, which is documented below and allows LIT to be extended with custom models, datasets, metrics, counterfactual generators, and more. The LIT server and components are provided as a library which users can use through their own demo binaries or via Colab.

The components can also be used as regular Python classes without starting a server; see below for details.

The LIT backend serves models, data, and interpretability components, each of which is a Python class implementing a minimal API and relying on the spec system to detect fields and verify compatibility. The server is stateless, but implements a caching layer for model predictions - this simplifies component design and allows interactive use of large models like BERT or T5.

The frontend is a stateful single-page app, built using Lit[1] for modularity and MobX for state management. It consists of a core UI framework, a set of shared “services” which manage persistent state, and a set of independent modules which render visualizations and support user interaction. For more details, see the UI guide and the frontend developer guide.

## Adding Models and Data

To run LIT with your own models and data, you can create a custom `demo.py` script that passes these to the LIT server. For example:

    def main(_):
      # MulitiNLIData implements the Dataset API
      datasets = {
          'mnli_matched': MultiNLIData('/path/to/dev_matched.tsv'),
          'mnli_mismatched': MultiNLIData('/path/to/dev_mismatched.tsv'),
      }

      # NLIModel implements the Model API
      models = {
          'model_foo': NLIModel('/path/to/model/foo/files'),
          'model_bar': NLIModel('/path/to/model/bar/files'),
      }

      lit_demo = lit_nlp.dev_server.Server(models, datasets, port=4321)
      lit_demo.serve()

    if __name__ == '__main__':
      main()

Conceptually, a dataset is just a list of examples and a model is just a function that takes examples and returns predictions. The `Dataset` and `Model` classes implement this, and provide metadata (see the type system) to describe themselves to other components.

For pre-built `demo.py` examples, check out https://github.com/PAIR-code/lit/tree/main/lit_nlp/examples

### Validating Models and Data

Datasets and models can optionally be validated by LIT to ensure that dataset examples match their spec and that model output values match their spec. This can be very helpful during development of new model and dataset wrappers to ensure correct behavior in LIT.

At LIT server startup, the `validate` flag can be used to enable validation. There are three modes:

- `--validate=first` will check the first example in each dataset.

- `--validate=sample` will validate a sample of 5% of each dataset.

- `--validate=all` will run validation on all examples from all datasets.

Additionally, if using LIT datasets and models outside of the LIT server, validation can be called directly through the `validation` module.

## Datasets

Datasets (`Dataset`) are just a list of examples, with associated type information following LIT’s type system.

- `spec()` should return a flat dict that describes the fields in each example

- `self._examples` should be a list of flat dicts

LIT operates on all examples loaded in the datasets you include in your LIT server, therefore you should take care to use dataset sizes that can fit into memory on your backend server and can be displayed in the browser.

NOTE: See the FAQ for more details on dataset size limitations.

Implementations should subclass `Dataset`. Usually this is just a few lines of code - for example, the following is a complete implementation for the MultiNLI dataset:

    class MultiNLIData(Dataset):
      """Loader for MultiNLI development set."""

      NLI_LABELS = ['entailment', 'neutral', 'contradiction']

      def __init__(self, path: str):
        # Read the eval set from a .tsv file as distributed with the GLUE benchmark.
        df = pandas.read_csv(path, sep='\t')
        # Store as a list of dicts, conforming to self.spec()
        self._examples = [{
          'premise': row['sentence1'],
          'hypothesis': row['sentence2'],
          'label': row['gold_label'],
          'genre': row['genre'],
        } for _, row in df.iterrows()]

      def spec(self) -> types.Spec:
        return {
          'premise': lit_types.TextSegment(),
          'hypothesis': lit_types.TextSegment(),
          'label': lit_types.CategoryLabel(vocab=self.NLI_LABELS),
          # We can include additional fields, which don't have to be used by the model.
          'genre': lit_types.CategoryLabel(),
        }

In this example, all four fields (premise, hypothesis, label, and genre) have string values, but the semantic types tell LIT a bit more about how to interpret them:

- `premise` and `hypothesis` should be treated as natural-language text (`TextSegment`)

- `label` should be treated as a categorical feature (`CategoryLabel`) with a fixed, known set of possible values (`vocab=self.NLI_LABELS`)

- `genre` should be treated as a categorical feature, but with an unknown or open set of values.

This implementation uses Pandas to read a TSV file, but you can also use services like TensorFlow Datasets - simply wrap them in your `__init__()` function.

Note that you can freely add additional features - such as `genre` in the example above - which the model may not be aware of. The LIT UI will recognize these features for slicing, binning, etc., and they will also be available to interpretation components such as custom metrics.

### Transformations

The `Dataset` class also supports a limited set of transformations, similar to TensorFlow’s tf.data.Dataset but more limited in scope and aimed at supporting quick iteration:

- `Dataset.slice[start:step:end]` will return a new `Dataset` with the same spec and a slice of the datapoints.

- `Dataset.sample(n, seed=42)` will return a new `Dataset` with the same spec and a random sample of the datapoints.

- `Dataset.remap(field_map: dict[str, str])` will return a new `Dataset` with renamed fields in both the examples and spec.

The latter is a shortcut to use datasets matching one model with another; for example, a dataset with a `"document"` field can be used with a model expecting a `"text"` input via `Dataset.remap({"document": "text"})`.[2]

## Models

Models (`Model`) are functions which take inputs and produce outputs, with associated type information following LIT’s type system. The core API consists of three methods:

- `input_spec()` should return a flat dict that describes necessary input fields

- `output_spec()` should return a flat dict that describes the model’s predictions and any additional outputs

- `predict()` should take a sequence of inputs (satisfying `input_spec()`) and yields a parallel sequence of outputs matching `output_spec()`.

Implementations should subclass `Model`. An example for MultiNLI might look something like:

    class NLIModel(Model):
      """Wrapper for a Natural Language Inference model."""

      NLI_LABELS = ['entailment', 'neutral', 'contradiction']

      def __init__(self, model_path: str, **kw):
        # Load the model into memory so we're ready for interactive use.
        self._model = _load_my_model(model_path, **kw)

      ##
      # LIT API implementations
      def predict(self, inputs: Iterable[Input]) -> Iterable[Preds]:
        """Predict on a stream of examples."""
        examples = [self._model.convert_dict_input(d) for d in inputs]  # any custom preprocessing
        return self._model.predict_examples(examples)  # returns a dict for each input

      def input_spec(self) -> types.Spec:
        """Describe the inputs to the model."""
        return {
            'premise': lit_types.TextSegment(),
            'hypothesis': lit_types.TextSegment(),
        }

      def output_spec(self) -> types.Spec:
        """Describe the model outputs."""
        return {
          # The 'parent' keyword tells LIT where to look for gold labels when computing metrics.
          'probas': lit_types.MulticlassPreds(vocab=NLI_LABELS, parent='label'),
        }

Unlike the dataset example, this model implementation is incomplete - you’ll need to customize `predict()` accordingly with any pre- or post-processing needed, such as tokenization.

Many deep learning models support a batched prediction behavior. Thus, we provide the `BatchedModel` class that implements simple batching. Users of this class must implement the `predict_minibatch()` function, which should convert a `Sequence` of `JsonDict` objects to the appropriate batch representation (typically, a `Mapping` of strings to aligned `Sequences` or `Tensors`) before calling the model. Optionally, you may want to override the `max_minibatch_size()` function, which determines the batch size.

Note: there are a few additional methods in the model API - see `Model` for details.

If your model is on a remote server, consider using the `BatchedRemoteModel` base class, which implements parallel batched requests using a thread pool.

### Adding more outputs

The above example defined a black-box model, with predictions but no access to internals. If we want a richer view into the model’s behavior, we can add additional return fields corresponding to hidden-state activations, gradients, word embeddings, attention, or more. For example, a BERT-based model with several such features might have the following `output_spec()`:

      def output_spec(self) -> types.Spec:
        """Describe the model outputs."""
        return {
          # The 'parent' keyword tells LIT where to look for gold labels when computing metrics.
          'probas': lit_types.MulticlassPreds(vocab=NLI_LABELS, parent='label'),
          # This model returns two different embeddings (activation vectors), but you can easily add more.
          'output_embs': lit_types.Embeddings(),      # from [CLS] token at top layer
          'mean_word_embs':  lit_types.Embeddings(),  # mean of input word embeddings
          # In LIT, we treat tokens as another model output. There can be more than one,
          # and the 'parent' field describes which input segment they correspond to.
          'premise_tokens': lit_types.Tokens(parent='premise'),
          'hypothesis_tokens': lit_types.Tokens(parent='hypothesis'),
          # Gradients are also returned by the model; 'align' here references a Tokens field.
          'premise_grad': lit_types.TokenGradients(align='premise_tokens'),
          'hypothesis_grad': lit_types.TokenGradients(align='hypothesis_tokens'),
          # Similarly, attention references a token field, but here we want the model's full "internal"
          # tokenization, which might be something like: [START] foo bar baz [SEP] spam eggs [END]
          'tokens': lit_types.Tokens(),
          'attention_layer0': lit_types.AttentionHeads(align=['tokens', 'tokens']),
          'attention_layer1': lit_types.AttentionHeads(align=['tokens', 'tokens']),
          'attention_layer2': lit_types.AttentionHeads(align=['tokens', 'tokens']),
          # ...and so on. Since the spec is just a dictionary of dataclasses, you can populate it
          # in a loop if you have many similar fields.
        }

The `predict()` function would return, for each example, additional dict entries corresponding to each of these fields.

Note: Because tokenization is often tightly coupled with the model code, we treat it as an intermediate state on the same level as embeddings or attention, and thus return `Tokens` as a field in the model output. This also allows models to expose different tokenizations for different inputs, such as `premise_tokens` and `hypothesis_tokens` above.

LIT components and frontend modules will automatically detect these spec fields and use them to support additional interpretation methods, such as the embedding projector or gradient-based salience maps.

You can also implement multi-headed models this way: simply add additional output fields for each prediction (such as another `MulticlassPreds`), and they’ll be automatically detected.

See the type system documentation for more details on available types and their semantics.

### Optional inputs

By default, LIT treats `input_spec` fields as required. However, this can be set to false if you wish to define optional model inputs. For example, a model that can accept pre-tokenized inputs might have the following spec:

        def input_spec(self) -> types.Spec:
          return {
              "text": lit_types.TextSegment(),
              "tokens": lit_types.Tokens(parent='text', required=False),
          }

And in the model’s `predict()`, you would have logic to use these and bypass the tokenizer:

        def predict(self, inputs: Iterable[Input]) -> Iterable[Preds]:
          input_tokens = [ex.get('tokens') or self.tokenizer.tokenize(ex['text'])
                          for ex in inputs]
          # ...rest of your predict logic...

`required=False` can also be used for label fields (such as `"label": lit_types.CategoryLabel(required=False)`), though these can also be omitted from the input spec entirely if they are not needed to compute model outputs.

## Interpretation Components

Backend interpretation components include metrics, salience maps, visualization aids like UMAP, and counterfactual generator plug-ins.

Most such components implement the `Interpreter` API. Conceptually, this is any function that takes a set of datapoints and a model, and produces some output.[3] For example, local gradient-based salience (GradientNorm) processes the `TokenGradients` and `Tokens` returned by a model and produces a list of scores for each token. The Integrated Gradients saliency method additionally requires a `TokenEmbeddings` input and corresponding output, as well as a label field `Target` to pin the gradient target to the same class as an input and corresponding output. See the GLUE models class for an example of these spec requirements.

The core API involves implementing the `run()` method:

      def run(self,
              inputs: list[JsonDict],
              model: lit_model.Model,
              dataset: lit_dataset.Dataset,
              model_outputs: Optional[list[JsonDict]] = None,
              config: Optional[JsonDict] = None):
        # config is any runtime options to this component, such as a threshold for
        # (binary) classification metrics.

Output from an interpreter component is unconstrained; it’s up to the frontend component requesting it to process the output correctly. In particular, some components (such as salience maps) may operate on each example independently, similar to model predictions, while others (such as metrics) may produce aggregate summaries of the input set.

Interpreters are also responsible for verifying compatibility by reading the model and dataset specs; these are also used to determine what fields to operate on. A typical implementation just loops over the relevant specs. For example, for simple gradient-based salience we might have:

      def find_fields(self, output_spec: Spec) -> list[str]:
        # Find TokenGradients fields
        grad_fields = utils.find_spec_keys(output_spec, types.TokenGradients)

        # Check that these are aligned to Tokens fields
        for f in grad_fields:
          tokens_field = output_spec[f].align  # pytype: disable=attribute-error
          assert tokens_field in output_spec
          assert isinstance(output_spec[tokens_field], types.Tokens)
        return grad_fields

      def run(self,
              inputs: list[JsonDict],
              model: lit_model.Model,
              dataset: lit_dataset.Dataset,
              model_outputs: Optional[list[JsonDict]] = None,
              config: Optional[JsonDict] = None) -> Optional[list[JsonDict]]:
        """Run this component, given a model and input(s)."""
        # Find gradient fields to interpret
        output_spec = model.output_spec()
        grad_fields = self.find_fields(output_spec)
        logging.info('Found fields for gradient attribution: %s', str(grad_fields))
        if len(grad_fields) == 0:  # pylint: disable=g-explicit-length-test
          return None

        # do rest of the work to create the salience maps for each available field

        # return a dtypes.TokenSalience for each input, which has a list of
        # tokens (from the model) and their associated scores.

This design adds some code overhead to interpretation components, but the benefit is flexibility - Python can be used to specify complex dependencies between fields, and multiple outputs can be easily supported in a loop.

### Metrics

For metrics, the `SimpleMetrics` class implements the spec-matching and input-unpacking logic to satisfy the general `Interpreter` API. A subclass of `SimpleMetrics` should implement an `is_compatible()` method and a `compute()` method, which is called on compatible (prediction, label) pairs and returns a dict of named score fields. For example:

    class RegressionMetrics(SimpleMetrics):
      """Standard regression metrics."""

      def is_compatible(self, field_spec: types.LitType) -> bool:
        """Return true if compatible with this field."""
        return isinstance(field_spec, types.RegressionScore)

      def compute(self,
                  labels: Sequence[float],
                  preds: Sequence[float],
                  label_spec: types.Scalar,
                  pred_spec: types.RegressionScore,
                  config: Optional[JsonDict] = None) -> dict[str, float]:
        """Compute metric(s) between labels and predictions."""
        del config
        mse = sklearn_metrics.mean_squared_error(labels, preds)
        pearsonr = scipy_stats.pearsonr(labels, preds)[0]
        spearmanr = scipy_stats.spearmanr(labels, preds)[0]
        return {'mse': mse, 'pearsonr': pearsonr, 'spearmanr': spearmanr}

The implementation of `SimpleMetrics.run()` uses the `parent` key (see type system) in fields of the model’s output spec to find the appropriate input fields to compare against, and calls `compute()` accordingly on the unpacked values.

### Generators

Conceptually, a generator is just an interpreter that returns new input examples. These may depend on the input only, as for techniques such as back- translation, or can involve feedback from the model, such as for adversarial attacks.

The core generator API is:

    class Generator(Interpreter):
      """Base class for LIT generators."""

      def generate_all(self,
                       inputs: list[JsonDict],
                       model: lit_model.Model,
                       dataset: lit_dataset.Dataset,
                       config: Optional[JsonDict] = None) -> list[list[JsonDict]]:
        """Run generation on a set of inputs.

        Args:
          inputs: sequence of inputs, following model.input_spec()
          model: optional model to use to generate new examples.
          dataset: optional dataset which the current examples belong to.
          config: optional runtime config.

        Returns:
          list of list of new generated inputs, following model.input_spec()
        """

Where the output is a list of lists: a set of generated examples for each input. For convenience, there is also a `generate()` method which takes a single example and returns a single list; we provide the more general `generate_all()` API to support model-based generators (such as back-translation) which benefit from batched requests.

As with other interpreter components, a generator can take custom arguments through `config`, such as the list of substitutions for the word replacer.

#### Backtranslator Generator

The backtranslator generator translates text segment inputs into foreign languages and back to the source language in order to create paraphrases. It relies on the Google Cloud Translate API to perform those translations. To use it, you must have a Google Cloud project and set up Cloud Translation as described at https://cloud.google.com/translate/docs/setup. Then, download your application credentials file locally and set the GOOGLE_APPLICATION_CREDENTIALS environment variable to point to that file. With that environment variable set to the correct path, LIT can make use of the backtranlator generator if you pass it as a generator in the Server constructor.

### Configuration UI

Interpreter components support an optional `config` option to specify run-time options, such as the number of samples for LIME or the pivot languages for back-translation. LIT provides a simple DSL to define these options, which will auto-generate a form on the frontend. The DSL uses the same type system as used to define data and model outputs, and the `config` argument will be passed a dict with the form values.

For example, the following spec:

      def config_spec(self) -> types.Spec:
        return {
            "Pivot languages": types.SparseMultilabel(
                vocab=['ar', 'bg', 'de', 'el', 'en', 'es', 'fr', 'hi', 'ru', 'sw',
                       'th', 'tr', 'ur', 'vi', 'zh'],
                default=['de', 'fr']),
            "Source language": types.TextSegment(default='en'),
        }

will give this form to configure back-translation:

Currently `config_spec()` is supported only for generators and salience methods, though any component can support the `config` argument to its `run()` method, which can be useful if running outside of the LIT UI.

The following types are supported (see interpreter_controls.ts):

- `Scalar`, which creates a slider for setting a numeric option. You can specify the `min_val`, `max_val`, `default`, and `step`, values for the slider through arguments to the `Scalar` constructor.

- `Boolean` (`BooleanLitType` in TypeScript), which creates a checkbox, with a `default` value to be set in the constructor.

- `CategoryLabel`, which creates a dropdown with options specified in the `vocab` argument.

- `SparseMultilabel`, which creates a series of checkboxes for each option specified in the `vocab` argument.

- `TextSegment`, which creates an input text box for string entry, with an optional default value from the `default` argument.

- `Tokens`, which creates an input text box for entry of multiple, comma-separated strings which are parsed into a list of strings to be supplied to the interpreter.

- `SingleFieldMatcher`, which acts like a `CategoryLabel` but where the vocab is automatically populated by the names of fields from the data or model spec. For example, `SingleFieldMatcher(spec='dataset', types=['TextSegment'])` will give a dropdown with the names of all `TextSegment` fields in the dataset.

- `MultiFieldMatcher` is similar to `SingleFieldMatcher` except it gives a set of checkboxes to select one or more matching field names. The returned value in `config` will be a list of string values.

The field matching controls can be useful for selecting one or more fields to operate on. For example,to choose which input fields to perturb, or which output field of a multi-head model to run an adversarial attack (such as HotFlip) against.

## Type System

LIT passes data around (e.g., between the server and the web app) as flat records with `string` keys. In Python types these are `Mapping[str, ...]` and in TypeScript types these are `{[key: string]: unknown}`. LIT serializes these records to JSON when communicating between the server and the web app client. It is because of this serialization that we introduced LIT’s type system; LIT needs a way to communicate how to process and understand the semantics of the shape and (allowable) values for the records being passed around in JSON’s more limited type system.

The shape of a record – its specific keys and the types of their values – is defined by a `Spec`; a `dict[str, LitType]`. Each `LitType` class has a `default` property whose type annotation describes the type of the value for that field in a JSON record. `LitType`s are implemented using hierarchical inheritance; the canonical types can be found in types.py, with parallel implementations in lit_types.ts.

### Conventions

LIT supports several different “kinds” of `Spec`s (input vs output vs meta, etc.), and their use in context has specific implications, described per base class below.

- `lit_nlp.api.dataset.Dataset`

  - `.spec() -> Spec` describes the shape of every record in the `Sequence` returned by `Dataset.examples()`.

  - `.init_spec() -> Optional[Spec]` describes the user-configurable arguments for loading a new instance of this `Dataset` class via the web app’s UI. Returning `None` or an empty `Spec` means that there is nothing configurable about how this `Dataset` is loaded, and it will not show up in the dataset loading section of the web app’s Global Settings.

- `lit_nlp.api.model.Model`

  - `.input_spec() -> Spec` describes the shape required of all records passed into the `Model.predict()` function via the `inputs` argument. LIT checks for compatibility between a `Dataset` and a `Model` by ensuring that `Model.input_spec()` is a subset of `Dataset.spec()`.

  - `.output_spec() -> Spec` describes the shape of all records returned by the `Model.predict()` function.

  - `.init_spec() -> Optional[Spec]` describes the user-configurable arguments for loading a new instance of this `Model` class via the web app’s UI. Returning `None` or an empty `Spec` means that there is nothing configurable about how this `model` is loaded, and it will not show up in the model loading section of the web app’s Global Settings.

- `lit_nlp.api.components.[Interpreter | Generator]`

  - `.config_spec() -> Spec` describes the user-configurable parameters for running this component. Returning an empty `Spec` means that this component always processes inputs in the same way.

  - `.meta_spec() -> Spec` is essentially unconstrained, but ideally describes the shape of the records returned by this component’s `.run()` method. Note that this `Spec` has different semantics depending on the component type. `Interpreter.run()` typically returns an `Iterable[Mapping[str, ...]]` of records (i.e., the `Mapping`) with this shape, because each input corresponds to one interpretation. Whereas `Generator.run()` typically returns an `Iterable[Iterable[Mapping[str, ...]]]` of records with this shape, because each input may enable the generation of one or more new examples.

- `lit_nlp.api.components.Metrics`

  - `.config_spec() -> Spec` describes the user-configurable parameters for running this component. Returning an empty `Spec` means that this component always processes inputs in the same way.

  - `.meta_spec() -> Spec` is a slight variation on the tradition `Spec`; it will always be a `Mapping[str, MetricResult]` describing the single record returned by the `Metrics.run()` method for each pair of compatible keys in the `Model.output_spec()` and `Dataset.spec()`. The `MetricResult` type also describes how to interpret the values in each record, e.g., if higher, lower, or numbers closer to zero are better.

Each `LitType` subclass encapsulates its own semantics (see types.py), but there are a few conventions all subclasses follow:

- The `align=` attribute references another field in the same spec and implies that both fields have index-aligned elements. For example, `Model.output_spec()` may contain `'tokens': lit_types.Tokens(...)` and `'pos': lit_types.SequenceTags(align='tokens')`, which references the “tokens” field. This implies that the “pos” field contains a corresponding value for every item in “tokens” and that you can access them with numeric indices. Transitively, this means that using `zip(..., strict=True)` (in Python 3.10 and above) will act as a pseudo-validator of this expectation.

- The `parent=` attribute is typically used by `LitType`s in a `Model.output_spec()`, and must be a field in the input spec (i.e. the `Dataset.spec()`) against which this field’s value will be compared. For example, the `Model.output_spec()` may contain `'probas': lit_types.MulticlassPreds(parent='label', ...)` and the `Dataset.spec()` may contain `'label': lit_types.CategoryLabel()`, which means that the `Dataset`’s “label” field contains the ground truth values for that example, and the class prediction in the “probas” field can be compared to this label, e.g., by multi-class metrics.

- The `vocab=` attribute is used to represent the allowable values for that field, such as a set of classes for a `MulticlassPreds` field, or the set of labels for a `CategoryLabel` field.

- A field that appears in both the model’s input and output specs is assumed to represent the same value. This pattern is used for model-based input manipulation. For example, a language model might output `'tokens': lit_types.Tokens(...)`, and accept as (optional) input `'tokens': lit_types.Tokens(required=False, ...)`. An interpretability component could take output from the former, swap one or more tokens (e.g. with `[MASK]`), and feed them in the corresponding input field to compute masked fills.

### Compatibility Checks

LIT’s type system plays a critical role in ensuring reliability of and interoperability between the `Model`, `Dataset`, `Interpreter`, `Generator`, and `Metrics` classes:

- The Model-Dataset compatibility check ensures that the `Model.input_spec()` is a subset of the `Dataset.spec()`. The base `Model` class provides a robust and universal implementation of this check in the `is_compatible_with_dataset()` API, but you can override this method in your `Model` subclass if you so choose.

- All `lit_nlp.api.components` classes provide an `is_compatible` API to check their compatibility against `Model`s and `Dataset`s, as appropriate. For example, the `WordReplacer` generator only checks against the `Dataset` spec because it does not depend on model outputs, whereas the `Curves` interpreter checks the `Model` and `Dataset` because it needs labeled predictions, and the `GradientDotInput` interpreter only checks against the `Model.output_spec()` because it needs data that only the model can provide.

The LIT web app also uses `Spec` based compatibility checks. Each TypeScript module defines a `shouldDisplayModule` function that returns `true` if any active model-dataset pair provides sufficient information to support the visualization methods encapsulated by that module. If this function returns `false`, the module is not displayed in the layout. Note that this can cause jitter (UI modules appearing, disappearing, reordering, resizing, etc.) when switching between models or datasets with heterogeneous `Spec`s.

When implementing your own LIT components and modules, you can use `utils.find_spec_keys()` (Python) and `findSpecKeys()` (TypeScript) to identify fields of interest in a `Spec`. These methods recognize and respect subclasses. For example, `utils.find_spec_keys(spec, Scalar)` will also match any `RegressionScore` fields, but `utils.find_spec_keys(spec, RegressionScore)` will not return all `Scalar` fields in the `Spec`.

Important: Compatibility checks are performed automatically when building the `LitMetadata` for an instance of `LitApp`, typically by calling `dev_server.Serve()`. These checks are not performed when using components in a raw Python context (e.g., Colab, Jupyter, a REPL), as described below, and it is encouraged that you call these explicitly to ensure compatibility and avoid chasing red herrings.

### An In-Depth Example

Consider the following example from the MNLI demo. The MultiNLI dataset might define the following `Spec`.

    # Dataset.spec()
    {
      "premise": lit_types.TextSegment(),
      "hypothesis": lit_types.TextSegment(),
      "label": lit_types.CategoryLabel(
          vocab=["entailment", "neutral", "contradiction"]
      ),
      "genre": lit_types.CategoryLabel(),
    }

An example record in this `Dataset` might be:

    # dataset.examples[0]
    {
      "premise": "Buffet and a la carte available.",
      "hypothesis": "It has a buffet."
      "label": "entailment",
      "genre": "travel",
    }

A classification model for this task might have the following `input_spec()` and `output_spec()`. Notice that the input spec is a subset of the `Dataset.spec()`, thus LIT considers these to be compatible.

    # model.input_spec()
    {
      "premise": lit_types.TextSegment(),
      "hypothesis": lit_types.TextSegment(),
    }

    # model.output_spec()
    {
      "probas": lit_types.MulticlassPreds(
          parent="label",
          vocab=["entailment", "neutral", "contradiction"]
      ),
    }

Running this model over the input might yield the following prediction.

    # model.predict([dataset.examples[0]])[0]
    {
      "probas": [0.967, 0.024, 0.009],
    }

Passing this input and the prediction to the `ClassificationResults` interpreter would yield additional human-readable information as follows.

    # classification_results.run(
    #     dataset.examples[:1], model, dataset, [prediction]
    # )[0]
    {
      "probas": {
          "scores": [0.967, 0.024, 0.009],
          "predicted_class": "entailment",
          "correct": True,
      },
    }

See the examples for more.

### Available types

The full set of `LitType`s is defined in types.py. Numeric types such as `Integer` and `Scalar` have predefined ranges that can be overridden using corresponding `min_val` and `max_val` attributes as seen here. The different types available in LIT are summarized in the table below.

Note: Bracket syntax, such as `<float>[num_tokens]`, refers to the shapes of NumPy arrays where each element inside the brackets is an integer.

Name | Description | Value Type
--- | --- | ---
`TextSegment` | Natural language text, untokenized. | `str`
`GeneratedText` | Untokenized text, generated from a model (such as seq2seq). | `str`
`URL` | TextSegment, but interpreted as a URL. | `str`
`GeneratedURL` | Generated TextSegment, but interpreted as a URL (i.e., it maye not be real/is inappropriate as a label). | `str`
`SearchQuery` | TextSegment, but interpreted as a search query. | `str`
`String` | Opaque string data; ignored by components such as perturbation methods that operate on natural language. | `str`
`ReferenceTexts` | Multiple texts, such as a set of references for summarization or MT. | `list[tuple[str, float]]`
`GeneratedTextCandidates` | Multiple generation candidates, such as beam search output from a seq2seq model. | `list[tuple[str, float]]`
`Tokens` | Tokenized text. | `list[str]`
`TokenTopKPreds` | Predicted tokens and their scores, as from a language model or seq2seq model. | `list[list[tuple[str, float]]]`
`Boolean` | Boolean value. | `bool`
`Scalar` | Scalar numeric value. | `float`
`Integer` | Integer, with a default range from -32768 to +32767. value. | `int`
`ImageBytes` | Image, represented by a base64 encoded string. LIT also provides `JPEGBytes` and `PNGBytes` types for those specific encodings. | `str`
`RegressionScore` | Scalar value, treated as a regression target or prediction. | `float`
`ReferenceScores` | Scores for one or more reference texts. | `list[float]`
`CategoryLabel` | Categorical label, from open or fixed vocabulary. | `str`
`MulticlassPreds` | Multiclass predicted probabilities. | `<float>[num_labels]`
`SparseMultilabel` | Multiple non-exclusive labels, such as a set of attributes. | `list[str]`
`SparseMultilabelPreds` | Sparse multi-label predictions, represented as scored candidates. | `list[tuple[str, float]]`
`SequenceTags` | Sequence tags, aligned to tokens. | `list[str]`
`SpanLabels` | Span labels, aligned to tokens. Each label is (i,j,label). | `list[SpanLabel]`
`EdgeLabels` | Edge labels, aligned to tokens. This is a general way to represent many structured prediction tasks, such as coreference or SRL. See https://arxiv.org/abs/1905.06316 | `list[EdgeLabel]`
`MultiSegmentAnnotations` | In-line byte-span annotations, which can span multiple text segments. | `list[AnnotationCluster]`
`Embeddings` | Fixed-length embeddings or model activations. | `<float>[emb_dim]`
`Gradients` | Gradients with respect to embeddings or model activations. | `<float>[emb_dim]`
`TokenEmbeddings` | Per-token embeddings or model activations. | `<float>[num_tokens, emb_dim]`
`TokenGradients` | Gradients with respect to per-token embeddings or model activations. | `<float>[num_tokens, emb_dim]`
`ImageGradients` | Gradients with respect to image pixels. | `<float>[image_height, image_width, color_channels]`
`AttentionHeads` | Attention heads, grouped by layer. | `<float>[num_heads, num_tokens, num_tokens]`

Values can be plain data, NumPy arrays, or custom dataclasses - see dtypes.py for further detail.

Note: Note that `String`, `Boolean` and `URL` types in Python are represented as `StringLitType`, `BooleanLitType` and `URLLitType` in TypeScript to avoid naming collisions with protected TypeScript keywords.

## Server Configuration

Some properties of the LIT frontend can be configured from Python as arguments to `dev_server.Server()`. These include:

- `page_title`: set a custom page title, such as “Coreference Demo”.

- `canonical_url`: set a “canonical” URL (such as a shortlink) that will be used as the base when copying links from the LIT UI.

- `default_layout`: set the default UI layout, by name. See `layout.ts` and the section below for available layouts.

- `demo_mode`: demo / kiosk mode, which disables some functionality (such as save/load datapoints) which you may not want to expose to untrusted users.

- `inline_doc`: a markdown string that will be rendered in a documentation module in the main LIT panel.

- `onboard_start_doc`: a markdown string that will be rendered as the first panel of the LIT onboarding splash-screen.

- `onboard_end_doc`: a markdown string that will be rendered as the last panel of the LIT onboarding splash-screen.

For detailed documentation, see server_flags.py.

Most Python components (such as `Model`, `Dataset`, and `Interpreter`) also have a `description()` method which can be used to specify a human-readable description or help text that will appear in the UI.

### Customizing the Layout

You can specify custom web app layouts from Python via the `layouts=` attribute. The value should be a `Mapping[str, LitCanonicalLayout]`, such as:

    LM_LAYOUT = layout.LitCanonicalLayout(
        upper={
            "Main": [
                modules.EmbeddingsModule,
                modules.DataTableModule,
                modules.DatapointEditorModule,
            ]
        },
        lower={
            "Predictions": [
                modules.LanguageModelPredictionModule,
                modules.ConfusionMatrixModule,
            ],
            "Counterfactuals": [modules.GeneratorModule],
        },
        description="Custom layout for language models.",
    )

You can pass this to the server as:

    lit_demo = dev_server.Server(
        models,
        datasets,
        # other args...
        layouts={"lm": LM_LAYOUT},
        **server_flags.get_flags())
    return lit_demo.serve()

For a full example, see `lm_demo.py`.

You can see the pre-configured layouts provided by LIT, as well as the list of modules that can be included in your custom layout in `layout.py`. A `LitCanonicalLayout` can be defined to achieve four different configurations of the major content areas:

- Single-panel: Define only the `upper=` parameter.

- Two-panel, upper/lower: Define the `upper=` and `lower=` parameters.

- Two-panel, left/right: Define the `left=` and `upper=` parameters; the `upper=` section will be shown on the right.

- Three-panel: Define the `left=`, `upper=`, and `lower=` parameters; the `upper=` and `lower=` sections will be shown on the right.

To use a specific layout by default for a given LIT instance, pass the key (e.g., “simple”, “default”, or the name of a custom layout) as a server flag when initializing LIT (`--default_layout=<layout>`) or by setting the default value for that flag in you `server.py` file, e.g., `flags.FLAGS.set_default('default_layout', 'my_layout_name')`. The layout can also be set on-the-fly with the `layout=` URL param, which will take precedence.

Note: The pre-configured layouts are added to every `LitApp` instance using dictionary comprehension where the Mapping passed to the `LitApp` constructor overrides the pre-configured layouts `Mapping`. Thus, you can remove or change these pre-configured layouts as you like by passing a `Mapping` where the values of `simple`, `default`, and/or `experimental` is `None` (to remove) or a `LitCanonicalLayout` instance (to override) as you desire.

## Accessing the LIT UI in Notebooks

As an alternative to running a LIT server and connecting to it through a web browser, LIT can be used directly inside of python notebook environments, such as Colab and Jupyter.

After installing LIT through pip, create a `lit_nlp.notebook.LitWidget` object, passing in a dict of models and a dict of datasets, similar to the `lit_nlp.dev_server.Server` constructor. You can optionally provide a height parameter that specifies the height in pixels to render the LIT UI.

Then, in its own output cell, call the `render` method on the widget object to render the LIT UI. The LIT UI can be rendered in multiple cells if desired. The LIT UI can also be rendered in its own browser tab, outside of the notebook, by passing the parameter `open_in_new_tab=True` to the `render` method. The `render` method can optionally take in a configuration object to specify certain options to render the LIT UI using, such as the selected layout, current display tab, dataset, and models. See notebook.py for details.

The widget has a `stop` method which shuts down the widget’s server. This can be important for freeing up resources if you plan to create multiple LIT widget instances in a single notebook. Stopping the server doesn’t disable the model and dataset instances used by the server; they can still be used in the notebook and take up the resources they require.

Check out an example notebook.

## Using LIT components outside of LIT

All LIT Python components (models, datasets, interpreters, metrics, generators, etc.) are standalone classes that do not depend on the serving framework. You can easily use them from Colab, in scripts, or in your libraries. This can also be handy for development, as you can test new models or components without needing to reload the server or click the UI.

For example, to view examples in a dataset:

    from lit_nlp.examples.datasets import glue
    dataset = glue.SST2Data('validation')
    print(dataset.examples)  # list of records {"sentence": ..., "label": ...}

And to run inference on a few of them:

    from lit_nlp.examples.models import glue_models

    model = glue_models.SST2Model("/path/to/model/files")
    preds = list(model.predict(dataset.examples[:5]))
    # will return records {"probas": ..., "cls_emb": ..., ...} for each input

Or to compute input salience using LIME:

    from lit_nlp.components import lime_explainer

    lime = lime_explainer.LIME()
    lime.run([dataset.examples[0]], model, dataset)
    # will return {"tokens": ..., "salience": ...} for each example given

For a full working example in Colab, see LIT_components_example.ipynb.

[1]

Naming is just a happy coincidence; the Learning Interpretability Tool is not related to the Lit projects.

[2]

We could solve this particular case by standardizing names, but one still needs to be explicit if there are multiple segments available, such as `"question"` and `"document"` for a QA task.

[3]

A trivial one might just run the model and return predictions, though in practice we have a separate endpoint for that.