### Example class docstring implementation

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

A complete example showing how to document a class and its methods using AllenNLP docstring conventions.

```python
class SentenceClassifier(Model):
    """
    A model for classifying sentences.

    This is based on [this paper](link-to-paper). The input is a sentence and
    the output is a score for each target label.

    # Parameters

    vocab : `Vocabulary`

    text_field_embedder : `TextFieldEmbedder`
        The text field embedder that will be used to create a representation of the
        source tokens.

    seq2vec_encoder : `Seq2VeqEncoder`
        This encoder will take the embeddings from the `text_field_embedder` and
        encode them into a vector which represents the un-normalized scores
        for the target labels.

    dropout : `Optional[float]`, optional (default = `None`)
        Optional dropout to apply to the text field embeddings before passing through
        the `seq2vec_encoder`.
    """

    def __init__(
        self,
        vocab: Vocabulary,
        text_field_embedder: TextFieldEmbedder,
        seq2vec_encoder: Seq2SeqEncoder,
        dropout: Optional[float] = None,
    ) -> None:
        pass

    def forward(
        self,
        tokens: TextFieldTensors,
        labels: Optional[Tensor] = None,
    ) -> Dict[str, Tensor]:
        """
        Runs a forward pass of the model, computing the predicted logits and also the loss
        when `labels` is provided.

        # Parameters

        tokens : `TextFieldTensors`
            The tokens corresponding to the source sequence.

        labels : `Optional[Tensor]`, optional (default = `None`)
            The target labels.

        # Returns

        `Dict[str, Tensor]`
            An output dictionary with keys for the `loss` and `logits`.
        """
        pass
```

--------------------------------

### Install AllenNLP from Source

Source: https://github.com/allenai/allennlp/blob/main/README.md

Commands to clone the repository and install the package in editable mode with development dependencies.

```bash
git clone https://github.com/allenai/allennlp.git
```

```bash
pip install -U pip setuptools wheel
pip install --editable .[dev,all]
```

--------------------------------

### Install AllenNLP with Pip

Source: https://github.com/allenai/allennlp/blob/main/README.md

Installs the AllenNLP library using pip. It is recommended to install PyTorch first by following instructions on pytorch.org.

```bash
pip install allennlp
```

--------------------------------

### Install dependencies in editable mode

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Install the package in editable mode so that local changes are immediately reflected in the virtual environment.

```bash
pip install -U pip setuptools wheel
pip install -e .[dev,all]
```

--------------------------------

### Install All Optional AllenNLP Packages via Pip

Source: https://github.com/allenai/allennlp/blob/main/README.md

Installs AllenNLP with all available optional dependencies using pip. This provides a comprehensive installation including models, semparse, server, and optuna.

```bash
pip install allennlp[all]
```

--------------------------------

### Install AllenNLP Models Package

Source: https://github.com/allenai/allennlp/blob/main/README.md

Installs the 'allennlp-models' package, which contains pre-trained NLP models and components for training and running official AllenNLP models.

```bash
pip install allennlp-models
```

--------------------------------

### Install All Optional AllenNLP Packages via Conda

Source: https://github.com/allenai/allennlp/blob/main/README.md

Installs AllenNLP and all its associated optional packages (models, semparse, server, optuna) using conda-forge. This is a convenient way to get a comprehensive AllenNLP setup.

```bash
conda install -c conda-forge allennlp-models allennlp-semparse allennlp-server allennlp-optuna
```

--------------------------------

### Install AllenNLP with Optional Checklist Package via Pip

Source: https://github.com/allenai/allennlp/blob/main/README.md

Installs AllenNLP along with the 'checklist' optional dependencies using pip. The '[checklist]' syntax specifies the extra requirements to install.

```bash
pip install allennlp[checklist]
```

--------------------------------

### Method with Alternative Return Section in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Example of a Python method that returns an integer. The documentation uses an alternative return section format.

```python
class SomeClass:
 | ...
 | def method_with_alternative_return_section(self) -> int
```

--------------------------------

### Install AllenNLP with Conda

Source: https://github.com/allenai/allennlp/blob/main/README.md

Installs AllenNLP and Python 3.8 using conda-forge. Ensure you have conda installed and a compatible Python version.

```bash
conda install -c conda-forge python=3.8 allennlp
```

--------------------------------

### Install AllenNLP with Optional Checklist Package via Conda

Source: https://github.com/allenai/allennlp/blob/main/README.md

Installs AllenNLP along with the 'checklist' optional package using conda-forge. For a full list of optional packages, refer to the documentation.

```bash
conda install -c conda-forge allennlp-checklist
```

--------------------------------

### Document Class-Level Variable in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Example of documenting a class-level variable within a Python class. This variable is assigned an integer value.

```python
class SomeClass:
 | ...
 | some_class_level_variable = 1
```

--------------------------------

### Define Global Variable in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Example of defining a global variable in Python. Used for storing simple values accessible throughout the module.

```python
SOME_GLOBAL_VAR = "Ahhhh I'm a global var!!"
```

--------------------------------

### Build and serve documentation

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Commands to build the API documentation or serve it locally for preview.

```bash
make build-docs
```

```bash
make serve-docs
```

--------------------------------

### Configure and Train with GradientDescentTrainer

Source: https://context7.com/allenai/allennlp/llms.txt

Sets up and runs the GradientDescentTrainer with specified optimizer, data loaders, epochs, and regularization.

```python
from allennlp.training import GradientDescentTrainer
from allennlp.training.optimizers import AdamOptimizer
from allennlp.training.learning_rate_schedulers import LinearWithWarmup
from allennlp.data import DataLoader

# Create trainer
trainer = GradientDescentTrainer(
    model=model,
    optimizer=AdamOptimizer(model.parameters(), lr=1e-4),
    data_loader=train_loader,
    validation_data_loader=val_loader,
    num_epochs=10,
    patience=3,  # Early stopping patience
    validation_metric="+accuracy",  # Maximize accuracy
    serialization_dir="output/model",
    cuda_device=0,
    grad_norm=1.0,  # Gradient clipping
    learning_rate_scheduler=LinearWithWarmup(
        optimizer=optimizer,
        num_epochs=10,
        num_steps_per_epoch=len(train_loader),
        warmup_steps=100
    )
)

# Train
metrics = trainer.train()
print(f"Best validation accuracy: {metrics['best_validation_accuracy']}")
```

--------------------------------

### Create Archive from Serialization Directory

Source: https://context7.com/allenai/allennlp/llms.txt

Package model weights and configuration into a .tar.gz archive file for distribution or evaluation.

```python
archive_model(
    serialization_dir="output/model",
    weights="best.th",  # Which weights file to include
    archive_path="model.tar.gz"  # Output path
)
```

--------------------------------

### Register and instantiate components

Source: https://context7.com/allenai/allennlp/llms.txt

Use the Registrable system to register custom classes for instantiation via configuration parameters.

```python
from allennlp.common import Registrable
from allennlp.common.from_params import FromParams

# Register a custom component
@Registrable.register("my_custom_encoder")
class MyCustomEncoder(Registrable):
    def __init__(self, input_dim: int, hidden_dim: int, dropout: float = 0.1):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.dropout = dropout

    def forward(self, inputs):
        # Implementation
        pass

# List available implementations
available = MyCustomEncoder.list_available()
print(available)  # ['my_custom_encoder']

# Instantiate by name
encoder = MyCustomEncoder.by_name("my_custom_encoder")(
    input_dim=300,
    hidden_dim=256
)

# Or from params
from allennlp.common import Params
params = Params({
    "type": "my_custom_encoder",
    "input_dim": 300,
    "hidden_dim": 256
})
encoder = MyCustomEncoder.from_params(params)
```

--------------------------------

### Run AllenNLP in Docker

Source: https://github.com/allenai/allennlp/blob/main/README.md

Commands to initialize the local directory and execute the AllenNLP Docker container with GPU support.

```bash
mkdir -p $HOME/.allennlp/
docker run --rm --gpus all -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:latest
```

```bash
docker run --rm --gpus all -v $HOME/.allennlp:/root/.allennlp allennlp/allennlp:latest test-install
```

--------------------------------

### List Docker Images

Source: https://github.com/allenai/allennlp/blob/main/README.md

Command to verify the presence of the built AllenNLP Docker image.

```text
REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE
allennlp/allennlp   latest              b66aee6cb593        5 minutes ago       2.38GB
```

--------------------------------

### Define Class with Decorator in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Example of defining a Python class decorated with `@dataclass`. This is a common pattern for creating data-holding classes.

```python
@dataclass
class ClassWithDecorator
```

--------------------------------

### Check remote configuration

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Verify the current remote repositories configured for your local clone.

```bash
git remote -v
```

--------------------------------

### Verify Current Version

Source: https://github.com/allenai/allennlp/blob/main/RELEASE_PROCESS.md

Check the current version configuration against the TAG environment variable.

```bash
python scripts/get_version.py current
```

--------------------------------

### Run code quality and testing tools

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Execute formatting, linting, and type-checking tools locally before submitting a pull request.

```bash
black .
```

```bash
flake8 .
```

```bash
make typecheck
```

--------------------------------

### Generate Release Notes

Source: https://github.com/allenai/allennlp/blob/main/RELEASE_PROCESS.md

Generate the release notes content to be copied into the GitHub release description.

```bash
python scripts/release_notes.py
```

--------------------------------

### Model.forward_on_instances

Source: https://context7.com/allenai/allennlp/llms.txt

Runs the model on a list of instances and returns human-readable outputs.

```APIDOC
## Model.forward_on_instances

### Description
Runs the model on a list of instances and returns human-readable outputs.

### Parameters
#### Request Body
- **instances** (List[Instance]) - Required - A list of instances to process.

### Response
#### Success Response (200)
- **outputs** (List[Dict]) - A list of dictionaries containing model predictions (e.g., logits, probs).
```

--------------------------------

### Create Sequence Labeling Instance (NER)

Source: https://context7.com/allenai/allennlp/llms.txt

Build an Instance for sequence labeling tasks like Named Entity Recognition, associating tags with tokens.

```python
from allennlp.data import Instance
from allennlp.data.fields import TextField, SequenceLabelField
from allennlp.data.tokenizers import Token

# Sequence labeling instance (NER)
tokens = [Token("John"), Token("lives"), Token("in"), Token("NYC")]
text_field = TextField(tokens, {"tokens": token_indexer})
tags = ["B-PER", "O", "O", "B-LOC"]
tags_field = SequenceLabelField(tags, text_field, "tags")
instance = Instance({"tokens": text_field, "tags": tags_field})
```

--------------------------------

### Clone the repository

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Use these commands to clone your fork of the AllenNLP repository locally.

```bash
git clone https://github.com/USERNAME/allennlp.git
```

```bash
git clone git@github.com:USERNAME/allennlp.git
```

--------------------------------

### Train Model with AllenNLP CLI

Source: https://context7.com/allenai/allennlp/llms.txt

Use the `allennlp train` command to train a model from a configuration file. Options include parameter overrides, resuming training, forcing directory overwrite, and dry runs.

```bash
# Basic training
allennlp train config.jsonnet -s output_dir
```

```bash
# Training with parameter overrides
allennlp train config.jsonnet -s output_dir --overrides '{"trainer.num_epochs": 10}'
```

```bash
# Resume training from checkpoint
allennlp train config.jsonnet -s output_dir --recover
```

```bash
# Force overwrite existing output directory
allennlp train config.jsonnet -s output_dir --force
```

```bash
# Dry run to validate config and show dataset statistics
allennlp train config.jsonnet -s output_dir --dry-run
```

--------------------------------

### Tag and Push Release

Source: https://github.com/allenai/allennlp/blob/main/RELEASE_PROCESS.md

Create a git tag for the release and push it to the remote repository.

```bash
git tag $TAG -m "Release $TAG" && git push --tags
```

--------------------------------

### Train Model Programmatically (File)

Source: https://context7.com/allenai/allennlp/llms.txt

Trains a model using `train_model_from_file` with a configuration file. Allows specifying overrides, forcing directory overwrite, and returning the model.

```python
from allennlp.commands.train import train_model_from_file

# Train a model from a config file
model = train_model_from_file(
    parameter_filename="experiments/text_classifier.jsonnet",
    serialization_dir="output/text_classifier",
    overrides='{"trainer.num_epochs": 5}',
    force=True,  # Overwrite existing directory
    return_model=True
)
```

```python
# Training with recovery from checkpoint
model = train_model_from_file(
    parameter_filename="experiments/my_model.jsonnet",
    serialization_dir="output/my_model",
    recover=True  # Resume from existing checkpoint
)
```

--------------------------------

### Build and Use Vocabulary

Source: https://context7.com/allenai/allennlp/llms.txt

Builds a vocabulary from instances, allowing for minimum count and maximum size constraints. Supports saving, loading, and creation from pretrained transformers.

```python
from allennlp.data import Vocabulary, Instance
from allennlp.data.fields import TextField, LabelField

# Build vocabulary from instances
instances = [instance1, instance2, instance3]
vocab = Vocabulary.from_instances(
    instances,
    min_count={"tokens": 2},  # Minimum count for tokens
    max_vocab_size={"tokens": 10000}  # Maximum vocabulary size
)

# Check vocabulary size
print(f"Token vocab size: {vocab.get_vocab_size('tokens')}")
print(f"Label vocab size: {vocab.get_vocab_size('labels')}")

# Convert tokens to indices
token_index = vocab.get_token_index("hello", namespace="tokens")
token = vocab.get_token_from_index(token_index, namespace="tokens")

# Save and load vocabulary
vocab.save_to_files("vocab_dir")
loaded_vocab = Vocabulary.from_files("vocab_dir")

# Create vocabulary from pretrained transformer
vocab = Vocabulary.from_pretrained_transformer("bert-base-uncased", namespace="tokens")
```

--------------------------------

### Python API: train_model_from_file

Source: https://context7.com/allenai/allennlp/llms.txt

Trains a model from a configuration file and saves results to the specified directory.

```APIDOC
## train_model_from_file

### Description
Trains a model from a configuration file and saves results to the specified directory.

### Method
```python
from allennlp.commands.train import train_model_from_file

# Train a model from a config file
model = train_model_from_file(
    parameter_filename="experiments/text_classifier.jsonnet",
    serialization_dir="output/text_classifier",
    overrides='{"trainer.num_epochs": 5}',
    force=True,  # Overwrite existing directory
    return_model=True
)

# Training with recovery from checkpoint
model = train_model_from_file(
    parameter_filename="experiments/my_model.jsonnet",
    serialization_dir="output/my_model",
    recover=True  # Resume from existing checkpoint
)
```
```

--------------------------------

### Format parameter docstrings

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Standard syntax for documenting parameters and optional parameters in docstrings.

```text
name : `type`
    Description of the parameter, indented by four spaces.
```

```text
name : `type`, optional (default = `default_value`)
    Description of the parameter, indented by four spaces.
```

--------------------------------

### Create MultiProcessDataLoader

Source: https://context7.com/allenai/allennlp/llms.txt

Initializes a MultiProcessDataLoader for efficient, multiprocessing-based data loading with configurable batch size, shuffling, and worker count.

```python
from allennlp.data import DataLoader, Vocabulary
from allennlp.data.data_loaders import MultiProcessDataLoader
from allennlp.data.samplers import BucketBatchSampler

# Create data loader
data_loader = MultiProcessDataLoader(
    reader=reader,
    data_path="data/train.jsonl",
    batch_size=32,
    shuffle=True,
    num_workers=4,  # Number of data loading processes
    max_instances_in_memory=10000  # For memory-efficient loading
)

# Index with vocabulary
data_loader.index_with(vocab)

# Iterate over batches
for batch in data_loader:
    tokens = batch["tokens"]
    labels = batch["label"]
    # tokens is a dict of tensors ready for the model
```

--------------------------------

### Create Instance with Metadata

Source: https://context7.com/allenai/allennlp/llms.txt

Augment an AllenNLP Instance with additional metadata, such as original text or document identifiers.

```python
from allennlp.data import Instance
from allennlp.data.fields import TextField, SequenceLabelField, MetadataField
from allennlp.data.tokenizers import Token

# With metadata
metadata = {"original_text": "John lives in NYC", "doc_id": 123}
instance = Instance({
    "tokens": text_field, # Assuming text_field is defined from previous example
    "tags": tags_field,   # Assuming tags_field is defined from previous example
    "metadata": MetadataField(metadata)
})
```

--------------------------------

### Instantiate DatasetReader

Source: https://context7.com/allenai/allennlp/llms.txt

Instantiate a DatasetReader by its registered name. Configure parameters like `max_tokens` during initialization. This reader will be used to load and process data into AllenNLP Instances.

```python
from allennlp.data import DatasetReader

# Instantiate reader
reader = DatasetReader.by_name("text_classification")(
    max_tokens=512
)
```

--------------------------------

### Index Instance and Convert to Tensors

Source: https://context7.com/allenai/allennlp/llms.txt

Index the fields of an AllenNLP Instance using a vocabulary and convert it into a tensor dictionary for model input.

```python
# Index instance with vocabulary
instance.index_fields(vocab)
tensor_dict = instance.as_tensor_dict()
```

--------------------------------

### Define experiments with Jsonnet

Source: https://context7.com/allenai/allennlp/llms.txt

Use Jsonnet files to define model architecture, data loading, and training parameters.

```jsonnet
// config.jsonnet
local embedding_dim = 300;
local hidden_dim = 256;

{
    "dataset_reader": {
        "type": "text_classification",
        "tokenizer": {
            "type": "whitespace"
        },
        "token_indexers": {
            "tokens": {
                "type": "single_id",
                "lowercase_tokens": true
            }
        }
    },
    "train_data_path": "data/train.jsonl",
    "validation_data_path": "data/dev.jsonl",
    "model": {
        "type": "text_classifier",
        "embedder": {
            "type": "basic",
            "token_embedders": {
                "tokens": {
                    "type": "embedding",
                    "embedding_dim": embedding_dim,
                    "trainable": true
                }
            }
        },
        "encoder": {
            "type": "lstm",
            "input_size": embedding_dim,
            "hidden_size": hidden_dim,
            "bidirectional": true
        }
    },
    "data_loader": {
        "batch_size": 32,
        "shuffle": true
    },
    "trainer": {
        "num_epochs": 20,
        "patience": 5,
        "cuda_device": 0,
        "optimizer": {
            "type": "adam",
            "lr": 0.001
        },
        "validation_metric": "+accuracy"
    }
}
```

--------------------------------

### Sync fork with upstream

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Update your local main branch with the latest changes from the upstream repository.

```bash
git checkout main  # if not already on main
git pull --rebase upstream main
git push
```

--------------------------------

### Create a new feature branch

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Create and push a new branch for your specific contribution.

```bash
# replace BRANCH with whatever name you want to give it
git checkout -b BRANCH
git push -u origin BRANCH
```

--------------------------------

### Load model archives

Source: https://context7.com/allenai/allennlp/llms.txt

Load saved model archives with optional configuration overrides.

```python
from allennlp.models.archival import load_archive, archive_model

# Load an archive
archive = load_archive(
    "model.tar.gz",
    cuda_device=0,
    overrides='{"model.dropout": 0.5}'
)
```

--------------------------------

### Function: func_with_args

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Documentation for the func_with_args function, which takes integer arguments.

```APIDOC
## func_with_args

### Description
This function has some args.

### Method
```python
def func_with_args(a: int, b: int, c: int = 3) -> int
```

### Parameters
#### Path Parameters
- __a__ (int) - Required - A number.
- __b__ (int) - Required - Another number.
- __c__ (int) - Optional - Yet another number. (default = 3)

### Notes
These are some notes.

### Returns
- int - The result of `a + b * c`.
```

--------------------------------

### Define Class with Constructor in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Illustrates the definition of a Python class with an initializer method. The constructor takes a float argument.

```python
class SomeClass:
 | def __init__(self) -> None
```

--------------------------------

### Set Release Tag Environment Variable

Source: https://github.com/allenai/allennlp/blob/main/RELEASE_PROCESS.md

Set the TAG environment variable to the desired version string before proceeding with the release.

```bash
export TAG='v1.0.0'
```

```fish
set -x TAG 'v1.0.0'
```

--------------------------------

### Run Model on Multiple Instances

Source: https://context7.com/allenai/allennlp/llms.txt

Process a list of instances using a loaded AllenNLP model. The model should be in evaluation mode. Outputs are dictionaries containing numpy arrays, useful for batch predictions.

```python
from allennlp.models import Model
from allennlp.models.archival import load_archive

# Load model
archive = load_archive("model.tar.gz", cuda_device=0)
model = archive.model
model.eval()

# Run on multiple instances
instances = [instance1, instance2, instance3]
outputs = model.forward_on_instances(instances)

# Each output is a dict with numpy arrays
for output in outputs:
    predicted_label = output["probs"].argmax()
    confidence = output["probs"].max()
    print(f"Predicted: {predicted_label}, Confidence: {confidence:.3f}")
```

--------------------------------

### Define Function with Arguments in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Shows how to define a Python function with typed arguments, including an optional argument with a default value. The function returns an integer.

```python
def func_with_args(a: int, b: int, c: int = 3) -> int
```

--------------------------------

### Method with Named Return Value in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Illustrates a Python method returning an integer, with the return value documented using a specific name. This is useful for clarity when multiple values could be returned.

```python
class SomeClass:
 | ...
 | def method_with_alternative_return_section3(self) -> int
```

--------------------------------

### Create Text Classification Instance

Source: https://context7.com/allenai/allennlp/llms.txt

Construct an AllenNLP Instance for text classification, including tokenized text and a label.

```python
from allennlp.data import Instance
from allennlp.data.fields import TextField, LabelField
from allennlp.data.tokenizers import Token

# Text classification instance
tokens = [Token("This"), Token("is"), Token("great")]
text_field = TextField(tokens, {"tokens": token_indexer})
label_field = LabelField("positive")
instance = Instance({"tokens": text_field, "label": label_field})
```

--------------------------------

### Class: SomeClass

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Documentation for the SomeClass class.

```APIDOC
## SomeClass

### Description
I'm a class!

### Constructor
```python
class SomeClass:
 | def __init__(self) -> None
```

### Parameters
#### Path Parameters
- __x__ (float) - Required - This attribute is called `x`.

### Class Variables
#### some_class_level_variable
```python
class SomeClass:
 | ...
 | some_class_level_variable = 1
```
This is how you document a class-level variable.

#### some_class_level_var_with_type
```python
class SomeClass:
 | ...
 | some_class_level_var_with_type: int = 1
```

### Methods
#### some_method
```python
class SomeClass:
 | ...
 | def some_method(self) -> None
```
I'm a method!
But I don't do anything.

##### Returns
- None

#### method_with_alternative_return_section
```python
class SomeClass:
 | ...
 | def method_with_alternative_return_section(self) -> int
```
Another method.

##### Returns
- A completely arbitrary number.

#### method_with_alternative_return_section3
```python
class SomeClass:
 | ...
 | def method_with_alternative_return_section3(self) -> int
```
Another method.

##### Returns
- number (int) - A completely arbitrary number.
```

--------------------------------

### Implement TextClassificationReader

Source: https://context7.com/allenai/allennlp/llms.txt

Create a custom DatasetReader for text classification tasks. Implement `_read` to parse data files and `text_to_instance` to convert text and labels into AllenNLP Instances. Supports tokenization and max token limits.

```python
from typing import Iterable, Dict
from allennlp.data import DatasetReader, Instance
from allennlp.data.fields import TextField, LabelField
from allennlp.data.tokenizers import Tokenizer, WhitespaceTokenizer
from allennlp.data.token_indexers import TokenIndexer, SingleIdTokenIndexer

@DatasetReader.register("text_classification")
class TextClassificationReader(DatasetReader):
    def __init__(
        self,
        tokenizer: Tokenizer = None,
        token_indexers: Dict[str, TokenIndexer] = None,
        max_tokens: int = None,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.tokenizer = tokenizer or WhitespaceTokenizer()
        self.token_indexers = token_indexers or {"tokens": SingleIdTokenIndexer()}
        self.max_tokens = max_tokens

    def _read(self, file_path: str) -> Iterable[Instance]:
        import json
        with open(file_path) as f:
            for line in f:
                data = json.loads(line)
                text = data["text"]
                label = data.get("label")
                yield self.text_to_instance(text, label)

    def text_to_instance(
        self,
        text: str,
        label: str = None,
    ) -> Instance:
        tokens = self.tokenizer.tokenize(text)
        if self.max_tokens:
            tokens = tokens[:self.max_tokens]

        fields = {"tokens": TextField(tokens, self.token_indexers)}
        if label is not None:
            fields["label"] = LabelField(label)

        return Instance(fields)
```

--------------------------------

### Commit and Push Release Changes

Source: https://github.com/allenai/allennlp/blob/main/RELEASE_PROCESS.md

Commit version updates and push them to the remote repository.

```bash
git commit -a -m "Prepare for release $TAG" && git push
```

--------------------------------

### Define Function with No Arguments in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Demonstrates defining a Python function that takes no arguments. Useful for simple utility functions.

```python
def func_with_no_args()
```

--------------------------------

### Add upstream remote

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Configure the main AllenNLP repository as an upstream remote to track changes.

```bash
git remote add upstream https://github.com/allenai/allennlp.git
```

--------------------------------

### AllenNLP CLI: train command

Source: https://context7.com/allenai/allennlp/llms.txt

The `train` command trains a model using a configuration file specifying the model architecture, dataset readers, and training parameters.

```APIDOC
## allennlp train

### Description
Trains a model using a configuration file specifying the model architecture, dataset readers, and training parameters.

### Usage
```bash
# Basic training
allennlp train config.jsonnet -s output_dir

# Training with parameter overrides
allennlp train config.jsonnet -s output_dir --overrides '{"trainer.num_epochs": 10}'

# Resume training from checkpoint
allennlp train config.jsonnet -s output_dir --recover

# Force overwrite existing output directory
allennlp train config.jsonnet -s output_dir --force

# Dry run to validate config and show dataset statistics
allennlp train config.jsonnet -s output_dir --dry-run
```
```

--------------------------------

### Model.load

Source: https://context7.com/allenai/allennlp/llms.txt

Loads a trained model from a serialization directory or an archive file.

```APIDOC
## Model.load

### Description
Loads a trained model from a serialization directory or archive file.

### Parameters
#### Request Body
- **config** (Params) - Required - Configuration parameters for the model.
- **serialization_dir** (str) - Required - Path to the directory containing model files.
- **cuda_device** (int) - Optional - Device ID to load the model onto (e.g., 0 for GPU).

### Request Example
{
  "config": "config.json",
  "serialization_dir": "output/model",
  "cuda_device": 0
}
```

--------------------------------

### Class: AnotherClassWithReallyLongConstructor

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Documentation for the AnotherClassWithReallyLongConstructor class, highlighting its long constructor arguments.

```APIDOC
## AnotherClassWithReallyLongConstructor

### Constructor
```python
class AnotherClassWithReallyLongConstructor:
 | def __init__(
 |     self,
 |     a_really_long_argument_name: int = 0,
 |     another_long_name: float = 2,
 |     these_variable_names_are_terrible: str = "yea I know",
 |     **kwargs
 | ) -> None
```
```

--------------------------------

### Train Model Programmatically (Params)

Source: https://context7.com/allenai/allennlp/llms.txt

Trains a model using `train_model` with a `Params` object for programmatic configuration. Allows modifying parameters before training.

```python
from allennlp.commands.train import train_model
from allennlp.common import Params

# Load params and train
params = Params.from_file("config.jsonnet")
params["trainer"]["num_epochs"] = 10

model = train_model(
    params=params,
    serialization_dir="output/model",
    force=True,
    return_model=True
)
```

--------------------------------

### Implement TextClassifier Model

Source: https://context7.com/allenai/allennlp/llms.txt

Implement a text classification model by extending the abstract Model class. Ensure the forward method returns a dictionary with at least a 'loss' key for training. Requires vocabulary, embedder, and encoder.

```python
from typing import Dict, Optional
import torch
from allennlp.data import Vocabulary
from allennlp.models import Model
from allennlp.modules import TextFieldEmbedder, Seq2VecEncoder
from allennlp.nn import util
from allennlp.training.metrics import CategoricalAccuracy

@Model.register("text_classifier")
class TextClassifier(Model):
    def __init__(
        self,
        vocab: Vocabulary,
        embedder: TextFieldEmbedder,
        encoder: Seq2VecEncoder,
        num_labels: int = None,
    ) -> None:
        super().__init__(vocab)
        self.embedder = embedder
        self.encoder = encoder
        num_labels = num_labels or vocab.get_vocab_size("labels")
        self.classifier = torch.nn.Linear(encoder.get_output_dim(), num_labels)
        self.accuracy = CategoricalAccuracy()
        self.loss = torch.nn.CrossEntropyLoss()

    def forward(
        self,
        tokens: Dict[str, torch.Tensor],
        label: torch.Tensor = None,
    ) -> Dict[str, torch.Tensor]:
        # Embed and encode
        embedded = self.embedder(tokens)
        mask = util.get_text_field_mask(tokens)
        encoded = self.encoder(embedded, mask)

        # Classify
        logits = self.classifier(encoded)
        probs = torch.softmax(logits, dim=-1)
        output = {"logits": logits, "probs": probs}

        if label is not None:
            output["loss"] = self.loss(logits, label)
            self.accuracy(logits, label)

        return output

    def get_metrics(self, reset: bool = False) -> Dict[str, float]:
        return {"accuracy": self.accuracy.get_metric(reset)}
```

--------------------------------

### Access AllenNLP Model Components

Source: https://context7.com/allenai/allennlp/llms.txt

Access the model, configuration, and dataset readers from a loaded archive object.

```python
model = archive.model
config = archive.config
dataset_reader = archive.dataset_reader
validation_dataset_reader = archive.validation_dataset_reader
```

--------------------------------

### Evaluate Model with AllenNLP CLI

Source: https://context7.com/allenai/allennlp/llms.txt

Use the `allennlp evaluate` command to evaluate a trained model. Supports specifying CUDA device, output files, and prediction outputs.

```bash
# Basic evaluation
allennlp evaluate model.tar.gz test_data.jsonl
```

```bash
# Evaluation on GPU with output file
allennlp evaluate model.tar.gz test_data.jsonl --cuda-device 0 --output-file metrics.json
```

```bash
# Evaluation with predictions output
allennlp evaluate model.tar.gz test_data.jsonl --predictions-output-file predictions.jsonl
```

```bash
# Evaluate multiple files
allennlp evaluate model.tar.gz "data1.jsonl,data2.jsonl" --output-file "out1.json,out2.json"
```

--------------------------------

### Function: func_with_no_args

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Documentation for the func_with_no_args function, which takes no arguments.

```APIDOC
## func_with_no_args

### Description
This function has no args.

### Method
```python
def func_with_no_args()
```
```

--------------------------------

### Create Custom Predictor

Source: https://context7.com/allenai/allennlp/llms.txt

Defines a custom predictor by subclassing Predictor and registering it. Includes methods for prediction and instance conversion.

```python
from allennlp.predictors import Predictor
from allennlp.common.util import JsonDict
from allennlp.data import Instance

@Predictor.register("sentiment_classifier")
class SentimentClassifierPredictor(Predictor):
    def predict(self, text: str) -> JsonDict:
        return self.predict_json({"text": text})

    def _json_to_instance(self, json_dict: JsonDict) -> Instance:
        text = json_dict["text"]
        return self._dataset_reader.text_to_instance(text)

    def predict_instance(self, instance: Instance) -> JsonDict:
        outputs = super().predict_instance(instance)
        # Add human-readable label
        label_idx = outputs["probs"].argmax()
        outputs["label"] = self._model.vocab.get_token_from_index(
            label_idx, namespace="labels"
        )
        return outputs
```

--------------------------------

### Predict with AllenNLP CLI

Source: https://context7.com/allenai/allennlp/llms.txt

Use the `allennlp predict` command for bulk predictions on an input file. Supports batch processing, using dataset readers, and silent mode.

```bash
# Basic prediction
allennlp predict model.tar.gz input.jsonl --output-file predictions.jsonl
```

```bash
# Prediction with batch processing on GPU
allennlp predict model.tar.gz input.jsonl --batch-size 32 --cuda-device 0
```

```bash
# Use dataset reader for input processing
allennlp predict model.tar.gz input.txt --use-dataset-reader
```

```bash
# Silent mode (no stdout output)
allennlp predict model.tar.gz input.jsonl --output-file predictions.jsonl --silent
```

--------------------------------

### Reset Local Tags After Failed Release

Source: https://github.com/allenai/allennlp/blob/main/RELEASE_PROCESS.md

Delete local tags and re-fetch from the remote to recover from a failed release process.

```bash
git tag -l | xargs git tag -d && git fetch -t
```

--------------------------------

### Load AllenNLP Model

Source: https://context7.com/allenai/allennlp/llms.txt

Load a trained AllenNLP model from a serialization directory or an archive file. Specify the CUDA device if needed. Use `Model.load` for directories and `Model.from_archive` for tar.gz files.

```python
from allennlp.models import Model
from allennlp.common import Params

# Load model from serialization directory
config = Params.from_file("output/model/config.json")
model = Model.load(
    config=config,
    serialization_dir="output/model",
    cuda_device=0  # Load onto GPU
)

# Load from archive file
model = Model.from_archive("model.tar.gz")
model.eval()

# Make predictions
outputs = model.forward_on_instance(instance)
```

--------------------------------

### Vocabulary API

Source: https://context7.com/allenai/allennlp/llms.txt

The Vocabulary class maps strings to integers for model input/output. It supports building vocabularies from instances, loading from files, and creating from pretrained transformers.

```APIDOC
## Vocabulary API

### Vocabulary

The Vocabulary class maps strings to integers for model input/output.

```python
from allennlp.data import Vocabulary, Instance
from allennlp.data.fields import TextField, LabelField

# Build vocabulary from instances
instances = [instance1, instance2, instance3]
vocab = Vocabulary.from_instances(
    instances,
    min_count={"tokens": 2},  # Minimum count for tokens
    max_vocab_size={"tokens": 10000}  # Maximum vocabulary size
)

# Check vocabulary size
print(f"Token vocab size: {vocab.get_vocab_size('tokens')}")
print(f"Label vocab size: {vocab.get_vocab_size('labels')}")

# Convert tokens to indices
token_index = vocab.get_token_index("hello", namespace="tokens")
token = vocab.get_token_from_index(token_index, namespace="tokens")

# Save and load vocabulary
vocab.save_to_files("vocab_dir")
loaded_vocab = Vocabulary.from_files("vocab_dir")

# Create vocabulary from pretrained transformer
vocab = Vocabulary.from_pretrained_transformer("bert-base-uncased", namespace="tokens")
```
```

--------------------------------

### Global Variable: SOME_GLOBAL_VAR

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Documentation for the global variable SOME_GLOBAL_VAR.

```APIDOC
## SOME_GLOBAL_VAR

### Description
This is a global var.

### Value
```python
SOME_GLOBAL_VAR = "Ahhhh I'm a global var!!"
```
```

--------------------------------

### AllenNLP CLI: evaluate command

Source: https://context7.com/allenai/allennlp/llms.txt

The `evaluate` command evaluates a trained model against a dataset and reports metrics.

```APIDOC
## allennlp evaluate

### Description
Evaluates a trained model against a dataset and reports metrics.

### Usage
```bash
# Basic evaluation
allennlp evaluate model.tar.gz test_data.jsonl

# Evaluation on GPU with output file
allennlp evaluate model.tar.gz test_data.jsonl --cuda-device 0 --output-file metrics.json

# Evaluation with predictions output
allennlp evaluate model.tar.gz test_data.jsonl --predictions-output-file predictions.jsonl

# Evaluate multiple files
allennlp evaluate model.tar.gz "data1.jsonl,data2.jsonl" --output-file "out1.json,out2.json"
```
```

--------------------------------

### Run unit tests with pytest

Source: https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md

Execute specific test modules locally using pytest.

```bash
pytest -v tests/nn/util_test.py
```

--------------------------------

### Evaluate Model from Archive File

Source: https://context7.com/allenai/allennlp/llms.txt

Use the Evaluator to assess model performance on a given dataset and save metrics and predictions.

```python
from allennlp.evaluation import Evaluator
from allennlp.commands.evaluate import evaluate_from_archive

# Evaluate from archive file
metrics = evaluate_from_archive(
    archive_file="model.tar.gz",
    input_file="data/test.jsonl",
    metrics_output_file="metrics.json",
    predictions_output_file="predictions.jsonl",
    cuda_device=0,
    batch_size=32
)

print(f"Test accuracy: {metrics['accuracy']:.4f}")
print(f"Test loss: {metrics['loss']:.4f}")
```

--------------------------------

### Define Class Method in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Demonstrates defining a method within a Python class. This method does not perform any action and returns None.

```python
class SomeClass:
 | ...
 | def some_method(self) -> None
```

--------------------------------

### DataLoader API

Source: https://context7.com/allenai/allennlp/llms.txt

MultiProcessDataLoader provides efficient data loading with multiprocessing and batching. It supports shuffling, worker processes, and memory-efficient loading.

```APIDOC
## DataLoader API

### MultiProcessDataLoader

Efficient data loading with multiprocessing and batching.

```python
from allennlp.data import DataLoader, Vocabulary
from allennlp.data.data_loaders import MultiProcessDataLoader
from allennlp.data.samplers import BucketBatchSampler

# Create data loader
data_loader = MultiProcessDataLoader(
    reader=reader,
    data_path="data/train.jsonl",
    batch_size=32,
    shuffle=True,
    num_workers=4,  # Number of data loading processes
    max_instances_in_memory=10000  # For memory-efficient loading
)

# Index with vocabulary
data_loader.index_with(vocab)

# Iterate over batches
for batch in data_loader:
    tokens = batch["tokens"]
    labels = batch["label"]
    # tokens is a dict of tensors ready for the model
```
```

--------------------------------

### Define Class with Long Constructor in Python

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Shows a Python class with a constructor that has multiple arguments, including keyword arguments. This demonstrates handling long argument lists.

```python
class AnotherClassWithReallyLongConstructor:
 | def __init__(
 |     self,
 |     a_really_long_argument_name: int = 0,
 |     another_long_name: float = 2,
 |     these_variable_names_are_terrible: str = "yea I know",
 |     **kwargs
 | ) -> None
```

--------------------------------

### Read Data Instances

Source: https://context7.com/allenai/allennlp/llms.txt

Reads all instances from a JSONL file into a list. For large datasets, iterate directly over the reader for lazy loading.

```python
instances = list(reader.read("data/train.jsonl"))
```

```python
for instance in reader.read("data/train.jsonl"):
    print(instance.fields["tokens"])
```

--------------------------------

### Python API: train_model

Source: https://context7.com/allenai/allennlp/llms.txt

Trains a model using a Params object directly for programmatic configuration.

```APIDOC
## train_model

### Description
Trains a model using a Params object directly for programmatic configuration.

### Method
```python
from allennlp.commands.train import train_model
from allennlp.common import Params

# Load params and train
params = Params.from_file("config.jsonnet")
params["trainer"]["num_epochs"] = 10

model = train_model(
    params=params,
    serialization_dir="output/model",
    force=True,
    return_model=True
)
```
```

--------------------------------

### Create Conda Virtual Environment

Source: https://github.com/allenai/allennlp/blob/main/README.md

Creates a new Conda environment named 'allennlp_env' with Python 3.8. This isolates AllenNLP and its dependencies from other Python projects.

```bash
conda create -n allennlp_env python=3.8
```

--------------------------------

### Class: ClassWithDecorator

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Documentation for the ClassWithDecorator class, which uses a decorator.

```APIDOC
## ClassWithDecorator

### Description
```python
@dataclass
class ClassWithDecorator
```

### Class Variables
#### x
```python
class ClassWithDecorator:
 | ...
 | x: int = None
```
```

--------------------------------

### AllenNLP CLI: predict command

Source: https://context7.com/allenai/allennlp/llms.txt

The `predict` command runs a trained model against a JSON-lines input file for bulk predictions.

```APIDOC
## allennlp predict

### Description
Runs a trained model against a JSON-lines input file for bulk predictions.

### Usage
```bash
# Basic prediction
allennlp predict model.tar.gz input.jsonl --output-file predictions.jsonl

# Prediction with batch processing on GPU
allennlp predict model.tar.gz input.jsonl --batch-size 32 --cuda-device 0

# Use dataset reader for input processing
allennlp predict model.tar.gz input.txt --use-dataset-reader

# Silent mode (no stdout output)
allennlp predict model.tar.gz input.jsonl --output-file predictions.jsonl --silent
```
```

--------------------------------

### Activate Conda Virtual Environment

Source: https://github.com/allenai/allennlp/blob/main/README.md

Activates the 'allennlp_env' Conda environment. You must activate this environment in each terminal session where you intend to use AllenNLP.

```bash
conda activate allennlp_env
```

--------------------------------

### Trainer API

Source: https://context7.com/allenai/allennlp/llms.txt

The GradientDescentTrainer is the main trainer class for supervised learning with gradient descent. It handles training loops, validation, and early stopping.

```APIDOC
## Trainer API

### GradientDescentTrainer

The main trainer class for supervised learning with gradient descent.

```python
from allennlp.training import GradientDescentTrainer
from allennlp.training.optimizers import AdamOptimizer
from allennlp.training.learning_rate_schedulers import LinearWithWarmup
from allennlp.data import DataLoader

# Create trainer
trainer = GradientDescentTrainer(
    model=model,
    optimizer=AdamOptimizer(model.parameters(), lr=1e-4),
    data_loader=train_loader,
    validation_data_loader=val_loader,
    num_epochs=10,
    patience=3,  # Early stopping patience
    validation_metric="+accuracy",  # Maximize accuracy
    serialization_dir="output/model",
    cuda_device=0,
    grad_norm=1.0,  # Gradient clipping
    learning_rate_scheduler=LinearWithWarmup(
        optimizer=optimizer,
        num_epochs=10,
        num_steps_per_epoch=len(train_loader),
        warmup_steps=100
    )
)

# Train
metrics = trainer.train()
print(f"Best validation accuracy: {metrics['best_validation_accuracy']}")
```
```

--------------------------------

### Document Class Field in Python Dataclass

Source: https://github.com/allenai/allennlp/blob/main/scripts/tests/py2md/basic_example_expected_output.md

Shows how to document a field within a Python dataclass. The field `x` is typed as an integer and initially set to None.

```python
class ClassWithDecorator:
 | ...
 | x: int = None
```