### Inspect Dataset Examples

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Prints a few examples from the dataset for verification.

```python
# inspect at a few examples
for ex in dataset.take(3):
  print(ex)
```

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

### Iterate through dataset samples

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Use this loop to inspect the first three examples from the dataset.

```python
for ex in dataset.take(3):
  print(ex)
```

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

### Setup TPU/GPU Training Infrastructure

Source: https://context7.com/google-research/bigbird/llms.txt

Commands for provisioning GCP resources and executing training scripts on TPU or GPU hardware.

```bash
# Create GCP instance with TPU
gcloud compute instances create bigbird \
    --zone=europe-west4-a \
    --machine-type=n1-standard-16 \
    --boot-disk-size=50GB \
    --image-project=ml-images \
    --image-family=tf-2-3-1 \
    --maintenance-policy=TERMINATE \
    --scopes=cloud-platform

# Create TPU
gcloud compute tpus create bigbird \
    --zone=europe-west4-a \
    --accelerator-type=v3-32 \
    --version=2.3.1

# SSH into instance
gcloud compute ssh --zone "europe-west4-a" "bigbird"

# Install BigBird
git clone https://github.com/google-research/bigbird.git
cd bigbird
pip3 install -e .

# Training with TPU
python -m bigbird.classifier.run_classifier \
    --use_tpu=True \
    --tpu_name=bigbird \
    --tpu_zone=europe-west4-a \
    --num_tpu_cores=32 \
    --data_dir="gs://your-bucket/data" \
    --output_dir="gs://your-bucket/output" \
    --init_checkpoint="gs://bigbird-transformer/bigbr_base/model.ckpt-0"

# Training with GPU (MirroredStrategy)
python -m bigbird.classifier.run_classifier \
    --use_tpu=False \
    --data_dir="/path/to/data" \
    --output_dir="/path/to/output"
```

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

### Install BigBird Library

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Installs the BigBird package from the official GitHub repository.

```python
!pip install git+https://github.com/google-research/bigbird.git -q
```

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

### Generate summary for an example

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Extracts the predicted summary from the first element of the example tuple.

```python
predicted_summary = summerize(ex[0])['pred_sent'][0]
```

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

### Big Bird Installation

Source: https://github.com/google-research/bigbird/blob/master/README.md

Clone the Big Bird repository and install it using pip. This also includes instructions for downloading pretrained checkpoints.

```bash
git clone https://github.com/google-research/bigbird.git
cd bigbird
pip3 install -e .

```

```bash
mkdir -p bigbird/ckpt
gsutil cp -r gs://bigbird-transformer/ bigbird/ckpt/

```

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

### Initialize ROUGE scorer and aggregator

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Setup the ROUGE scorer with specific metrics and initialize a bootstrap aggregator for score collection.

```python
scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeLsum"], use_stemmer=True)
aggregator = scoring.BootstrapAggregator()
```

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

### Fine-tune BigBird for Text Classification

Source: https://context7.com/google-research/bigbird/llms.txt

Commands to install dependencies, download checkpoints, and train/export a classification model.

```bash
# Install dependencies
pip install bigbird tensorflow tensorflow-datasets tensorflow-text natsort

# Download pretrained checkpoint
mkdir -p bigbird/ckpt
gsutil cp -r gs://bigbird-transformer/bigbr_base bigbird/ckpt/

# Train on IMDB sentiment classification
python -m bigbird.classifier.run_classifier \
    --data_dir="tfds://imdb_reviews/plain_text" \
    --output_dir="/tmp/bigbird_imdb" \
    --init_checkpoint="bigbird/ckpt/bigbr_base/model.ckpt-0" \
    --max_encoder_length=4096 \
    --num_labels=2 \
    --train_batch_size=4 \
    --learning_rate=1e-5 \
    --num_train_steps=16000 \
    --num_warmup_steps=1000 \
    --attention_type="block_sparse" \
    --do_train=True \
    --do_eval=True

# Export trained model
python -m bigbird.classifier.run_classifier \
    --output_dir="/tmp/bigbird_imdb" \
    --max_encoder_length=4096 \
    --do_export=True
```

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

### Prepare Input Data

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Builds the input function and creates a dataset for training.

```python
train_input_fn = run_classifier.input_fn_builder(
        data_dir=FLAGS.data_dir,
        vocab_model_file=FLAGS.vocab_model_file,
        max_encoder_length=FLAGS.max_encoder_length,
        substitute_newline=FLAGS.substitute_newline,
        is_training=True)
dataset = train_input_fn({'batch_size': 8})
```

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

### Create GCP Instance for Big Bird

Source: https://github.com/google-research/bigbird/blob/master/README.md

Commands to create a Google Cloud Platform instance with specified machine type, disk size, and TensorFlow image. It also includes creating a TPU and SSHing into the instance.

```bash
gcloud compute instances create \
  bigbird \
  --zone=europe-west4-a \
  --machine-type=n1-standard-16 \
  --boot-disk-size=50GB \
  --image-project=ml-images \
  --image-family=tf-2-3-1 \
  --maintenance-policy TERMINATE \
  --restart-on-failure \
  --scopes=cloud-platform

gcloud compute tpus create \
  bigbird \
  --zone=europe-west4-a \
  --accelerator-type=v3-32 \
  --version=2.3.1

gcloud compute ssh --zone "europe-west4-a" "bigbird"

```

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

### Import Dependencies and Initialize Flags

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Import necessary modules and initialize command-line flags for the BigBird environment.

```python
from bigbird.core import flags
from bigbird.core import modeling
from bigbird.core import utils
from bigbird.summarization import run_summarization
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
import tensorflow_text as tft
from tqdm import tqdm
import sys

FLAGS = flags.FLAGS
if not hasattr(FLAGS, "f"): flags.DEFINE_string("f", "", "")
FLAGS(sys.argv)

tf.enable_v2_behavior()
```

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

### Initialize Tokenizer

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Creates a Sentencepiece tokenizer instance using the provided vocabulary file.

```python
tokenizer = tft.SentencepieceTokenizer(
        model=tf.io.gfile.GFile(FLAGS.vocab_model_file, "rb").read())
```

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

### Build Input Pipeline

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Construct the input function and dataset for training.

```python
train_input_fn = run_summarization.input_fn_builder(
        data_dir=FLAGS.data_dir,
        vocab_model_file=FLAGS.vocab_model_file,
        max_encoder_length=FLAGS.max_encoder_length,
        max_decoder_length=FLAGS.max_decoder_length,
        substitute_newline=FLAGS.substitute_newline,
        is_training=True)
dataset = train_input_fn({'batch_size': 8})
```

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

### Execute Training Step

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Runs the forward and backward pass on a batch of data and prints the loss.

```python
loss, log_probs, grads = fwd_bwd(ex[0], ex[1])
print('Loss: ', loss.numpy())
```

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

### Initialize Eager Variable Store

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Create an eager variable store to manage model variables.

```python
from tensorflow.python.ops.variable_scope import EagerVariableStore
container = EagerVariableStore()
```

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

### Initialize Model and Classifier

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Creates the BertModel and the classifier loss layer based on the current flag configuration.

```python
bert_config = flags.as_dictionary()

model = modeling.BertModel(bert_config)
headl = run_classifier.ClassifierLossLayer(
        bert_config["hidden_size"], bert_config["num_labels"],
        bert_config["hidden_dropout_prob"],
        utils.create_initializer(bert_config["initializer_range"]),
        name=bert_config["scope"]+"/classifier")
```

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

### Execute Training Step

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Run a single training step using the defined forward/backward function.

```python
loss, llh, logits, pred_ids, grads = fwd_bwd(ex[0], ex[1])
print('Loss: ', loss)
```

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

### Configure Evaluation Input

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Sets up the input pipeline for evaluation using the classifier input builder.

```python
eval_input_fn = run_classifier.input_fn_builder(
        data_dir=FLAGS.data_dir,
        vocab_model_file=FLAGS.vocab_model_file,
        max_encoder_length=FLAGS.max_encoder_length,
        substitute_newline=FLAGS.substitute_newline,
        is_training=False)
eval_dataset = eval_input_fn({'batch_size': 8})
```

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

### Run Big Bird Classification Experiment

Source: https://github.com/google-research/bigbird/blob/master/README.md

Set environment variables for GCP project name and experiment bucket, then execute the base size classification script.

```shell
export GCP_PROJECT_NAME=bigbird-project  # Replace by your project name
export GCP_EXP_BUCKET=gs://bigbird-transformer-training/  # Replace
sh -x bigbird/classifier/base_size.sh

```

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

### Pretrained Model Path

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Reference path for loading a pretrained model checkpoint.

```python
# For training from scratch use
# ckpt_path = 'gs://bigbird-transformer/pretrain/bigbr_base/model.ckpt-0'
```

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

### Print comparison of article and summaries

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Displays the original article, the model's predicted summary, and the ground truth summary for verification.

```python
print('Article:\n {}\n\n Predicted summary:\n {}\n\n Ground truth summary:\n {}\n\n'.format(
    ex[0].numpy(),
    predicted_summary.numpy(),
    ex[1].numpy()))
```

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

### Import Dependencies and Initialize Flags

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Imports necessary modules and initializes TensorFlow flags for the BigBird environment.

```python
from bigbird.core import flags
from bigbird.core import modeling
from bigbird.core import utils
from bigbird.classifier import run_classifier
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
from tqdm import tqdm
import sys

FLAGS = flags.FLAGS
if not hasattr(FLAGS, "f"): flags.DEFINE_string("f", "", "")
FLAGS(sys.argv)

tf.enable_v2_behavior()
```

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

### Initialize DecoderStack

Source: https://context7.com/google-research/bigbird/llms.txt

Imports necessary modules for the Transformer decoder stack.

```python
from bigbird.core import decoder
from bigbird.core import utils
import tensorflow as tf
```

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

### Initialize and Use EmbeddingLayer

Source: https://context7.com/google-research/bigbird/llms.txt

Creates an embedding layer for token and position processing and performs a forward pass.

```python
from bigbird.core import utils
import tensorflow as tf

# Create embedding layer
embedding_layer = utils.EmbeddingLayer(
    vocab_size=32000,
    emb_dim=768,
    initializer=utils.create_initializer(0.02),
    scale_emb=False,  # Scale by sqrt(hidden_dim) for Pegasus
    use_token_type=True,  # For BERT-style segment embeddings
    num_token_types=2,
    use_position_embeddings=True,
    max_position_embeddings=4096,
    dropout_prob=0.1
)

# Forward pass
batch_size, seq_length = 2, 1024
input_ids = tf.random.uniform([batch_size, seq_length], 0, 32000, dtype=tf.int32)
token_type_ids = tf.zeros_like(input_ids)

embeddings = embedding_layer(
    input_ids=input_ids,
    seq_length=seq_length,
    start_pos=0,  # For incremental decoding
    token_type_ids=token_type_ids,
    training=True
)
# embeddings: [batch_size, seq_length, emb_dim]

# Linear projection back to vocabulary (for language modeling)
logits = embedding_layer.linear(embeddings)
# logits: [batch_size, seq_length, vocab_size]
```

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

### Training Loop

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Executes the training process using an Adam optimizer and tracks loss metrics.

```python
opt = tf.keras.optimizers.Adam(FLAGS.learning_rate)
train_loss = tf.keras.metrics.Mean(name='train_loss')

for i, ex in enumerate(tqdm(dataset.take(FLAGS.num_train_steps), position=0)):
  loss, llh, logits, pred_ids, grads = fwd_bwd(ex[0], ex[1])
  opt.apply_gradients(zip(grads, model.trainable_weights))
  train_loss(loss)
  if i% 10 == 0:
    print('Loss = {} '.format(train_loss.result().numpy()))
```

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

### Fine-tune BigBird for Document Summarization

Source: https://context7.com/google-research/bigbird/llms.txt

Commands to download Pegasus-style checkpoints and train the summarization model on PubMed data.

```bash
# Download Pegasus-style pretrained checkpoint
gsutil cp -r gs://bigbird-transformer/bigbp_large bigbird/ckpt/

# Train on PubMed scientific paper summarization
python -m bigbird.summarization.run_summarization \
    --data_dir="tfds://scientific_papers/pubmed" \
    --output_dir="/tmp/bigbird_pubmed" \
    --init_checkpoint="bigbird/ckpt/bigbp_large/model.ckpt-0" \
    --max_encoder_length=4096 \
    --max_decoder_length=256 \
    --attention_type="block_sparse" \
    --norm_type="prenorm" \
    --vocab_model_file="pegasus" \
    --train_batch_size=4 \
    --learning_rate=0.32 \
    --optimizer="Adafactor" \
    --num_train_steps=100000 \
    --num_warmup_steps=10000 \
    --beam_size=5 \
    --alpha=0.8 \
    --label_smoothing=0.1 \
    --do_train=True
```

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

### Execute Training Loop

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Performs model training steps using the Adam optimizer and tracks loss and accuracy metrics.

```python
opt = tf.keras.optimizers.Adam(FLAGS.learning_rate)
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')

for i, ex in enumerate(tqdm(dataset.take(FLAGS.num_train_steps), position=0)):
  loss, log_probs, grads = fwd_bwd(ex[0], ex[1])
  opt.apply_gradients(zip(grads, model.trainable_weights+headl.trainable_weights))
  train_loss(loss)
  train_accuracy(tf.one_hot(ex[1], 2), log_probs)
  if i% 200 == 0:
    print('Loss = {}  Accuracy = {}'.format(train_loss.result().numpy(), train_accuracy.result().numpy()))
```

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

### Initialize MultiHeadedAttentionLayer

Source: https://context7.com/google-research/bigbird/llms.txt

Configures the attention layer with specific block sparse parameters and performs a forward pass with required masks.

```python
from bigbird.core import attention
from bigbird.core import utils
import tensorflow as tf

# Configure attention layer
num_heads = 12
size_per_head = 64
seq_length = 4096
block_size = 64

# Create block sparse attention layer
attn_layer = attention.MultiHeadedAttentionLayer(
    attention_type='block_sparse',  # 'original_full', 'simulated_sparse', 'block_sparse'
    num_attention_heads=num_heads,
    size_per_head=size_per_head,
    num_rand_blocks=3,
    from_seq_length=seq_length,
    to_seq_length=seq_length,
    from_block_size=block_size,
    to_block_size=block_size,
    attention_probs_dropout_prob=0.1,
    initializer_range=0.02,
    use_bias=True,
    seed=42
)

# Prepare inputs
batch_size = 2
hidden_size = num_heads * size_per_head
from_tensor = tf.random.normal([batch_size, seq_length, hidden_size])
to_tensor = from_tensor  # Self-attention

# Create attention masks for block sparse attention
input_mask = tf.ones([batch_size, seq_length])
blocked_mask = tf.reshape(input_mask, [batch_size, seq_length // block_size, block_size])
from_mask = tf.reshape(input_mask, [batch_size, 1, seq_length, 1])
to_mask = tf.reshape(input_mask, [batch_size, 1, 1, seq_length])
band_mask = attention.create_band_mask_from_inputs(blocked_mask, blocked_mask)

masks = [None, band_mask, from_mask, to_mask, blocked_mask, blocked_mask]

# Forward pass
context_output = attn_layer(
    from_tensor=from_tensor,
    to_tensor=to_tensor,
    masks=masks,
    training=True
)
```

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

### Configure and Execute BigBird Decoder

Source: https://context7.com/google-research/bigbird/llms.txt

Initializes the decoder stack with specific hyperparameters and performs a forward pass using random input tensors.

```python
params = {
    'hidden_size': 768,
    'num_hidden_layers': 12,
    'num_attention_heads': 12,
    'intermediate_size': 3072,
    'hidden_act': 'gelu',
    'attention_probs_dropout_prob': 0.1,
    'hidden_dropout_prob': 0.1,
    'initializer_range': 0.02,
    'norm_type': 'prenorm',
    'use_bias': True,
    'use_gradient_checkpointing': False,
    'couple_encoder_decoder': False,  # Separate encoder/decoder weights
}

# Create decoder stack
decoder_stack = decoder.DecoderStack(params)

# Prepare inputs
batch_size = 2
encoder_length = 4096
decoder_length = 256
hidden_size = params['hidden_size']

decoder_inputs = tf.random.normal([batch_size, decoder_length, hidden_size])
encoder_outputs = tf.random.normal([batch_size, encoder_length, hidden_size])
encoder_mask = tf.ones([batch_size, encoder_length])

# Create causal self-attention mask
self_attention_mask = decoder.create_self_attention_mask(decoder_length)

# Forward pass
decoder_output = decoder_stack(
    decoder_inputs=decoder_inputs,
    self_attention_mask=self_attention_mask,
    encoder_outputs=encoder_outputs,
    encoder_mask=encoder_mask,
    cache=None,  # For incremental decoding
    decode_i=None,
    training=True
)
# decoder_output: [batch_size, decoder_length, hidden_size]
```

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

### Display Results

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Prints the original article, predicted summary, and ground truth summary using the tokenizer.

```python
print('Article:\n {}\n\n Predicted summary:\n {}\n\n Ground truth summary:\n {}\n\n'.format(
    tokenizer.detokenize(ex[0]),
    tokenizer.detokenize(pred_ids),
    tokenizer.detokenize(ex[1])))
```

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

### License Header

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Standard Apache License 2.0 header for BigBird source files.

```python
# Copyright 2020 The BigBird Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
```

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

### Import TensorFlow and Libraries

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Imports necessary TensorFlow modules, TensorFlow Datasets, and TensorFlow Text. Ensures TensorFlow v2 behavior is enabled.

```python
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
import tensorflow_text as tft
from tqdm import tqdm

tf.enable_v2_behavior()
```

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

### Instantiate Transformer Model

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Create the Transformer model instance within the variable container.

```python
with container.as_default():
  model = modeling.TransformerModel(transformer_config)
```

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

### Manage BigBird Configurations

Source: https://context7.com/google-research/bigbird/llms.txt

Accesses default hyperparameters and defines custom configuration dictionaries for model architecture and training.

```python
from bigbird.core import flags
from bigbird.core import utils

# Access default configuration
default_config = utils.get_default_config()
print(f"Default attention type: {default_config['attention_type']}")
print(f"Default block size: {default_config['block_size']}")
print(f"Default hidden size: {default_config['hidden_size']}")

# Configuration dictionary structure:
config = {
    # Transformer architecture
    'hidden_size': 768,
    'num_hidden_layers': 12,
    'num_attention_heads': 12,
    'intermediate_size': 3072,
    'hidden_act': 'gelu',
    'max_position_embeddings': 4096,

    # BigBird sparse attention
    'attention_type': 'block_sparse',  # 'original_full', 'simulated_sparse', 'block_sparse'
    'block_size': 64,  # Size of attention blocks
    'num_rand_blocks': 3,  # Random attention blocks per row

    # Normalization
    'norm_type': 'postnorm',  # 'prenorm' (Pegasus) or 'postnorm' (BERT)

    # Regularization
    'attention_probs_dropout_prob': 0.1,
    'hidden_dropout_prob': 0.1,
    'initializer_range': 0.02,

    # Sequence lengths
    'max_encoder_length': 4096,
    'max_decoder_length': 256,

    # Decoder settings (for seq2seq)
    'beam_size': 5,
    'alpha': 0.8,  # Length normalization
    'label_smoothing': 0.1,
}

# Save and load configuration
# flags.save('/path/to/config.json')  # Save current flags
# flags.load('/path/to/config.json')  # Load saved flags
```

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

### Instantiate BigBird Encoder Layers

Source: https://github.com/google-research/bigbird/blob/master/README.md

Alternatively, instantiate only the layers of the BigBird encoder for more granular control.

```python
from bigbird.core import encoder

only_layers = encoder.EncoderStack(...)
```

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

### Configure and Run BigBird Encoder Stack

Source: https://context7.com/google-research/bigbird/llms.txt

Defines the hyperparameter dictionary and executes a forward pass through the encoder stack.

```python
params = {
    'hidden_size': 768,
    'num_hidden_layers': 12,
    'num_attention_heads': 12,
    'intermediate_size': 3072,
    'hidden_act': 'gelu',
    'attention_probs_dropout_prob': 0.1,
    'hidden_dropout_prob': 0.1,
    'initializer_range': 0.02,
    'max_encoder_length': 4096,
    'attention_type': 'block_sparse',  # BigBird sparse attention
    'norm_type': 'postnorm',  # BERT-style post-normalization
    'block_size': 64,
    'num_rand_blocks': 3,
    'use_bias': True,
    'use_gradient_checkpointing': False,  # Memory optimization
}

# Create encoder stack
encoder_stack = encoder.EncoderStack(params)

# Prepare inputs
batch_size, seq_length = 2, 4096
hidden_size = params['hidden_size']
encoder_inputs = tf.random.normal([batch_size, seq_length, hidden_size])
encoder_mask = tf.ones([batch_size, seq_length])  # 1 for valid tokens, 0 for padding

# Forward pass through encoder layers
encoder_output = encoder_stack(
    encoder_inputs=encoder_inputs,
    encoder_inputs_mask=encoder_mask,
    training=True
)
```

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

### Instantiate BigBird Encoder

Source: https://github.com/google-research/bigbird/blob/master/README.md

Use this code to directly instantiate the BigBird encoder, which can replace BERT's encoder.

```python
from bigbird.core import modeling

bigb_encoder = modeling.BertModel(...)
```

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

### Load Pretrained BigBird Model

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Initializes model weights from a specified checkpoint path using a TensorFlow checkpoint reader.

```python
ckpt_path = 'gs://bigbird-transformer/pretrain/bigbr_base/model.ckpt-0'
ckpt_reader = tf.compat.v1.train.NewCheckpointReader(ckpt_path)
model.set_weights([ckpt_reader.get_tensor(v.name[:-2]) for v in tqdm(model.trainable_weights, position=0)])
```

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

### Convert Flags to Dictionary

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Export the current flag configuration into a dictionary format.

```python
transformer_config = flags.as_dictionary()
```

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

### Evaluation Input Pipeline

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Configures the input function and dataset for model evaluation.

```python
eval_input_fn = run_summarization.input_fn_builder(
        data_dir=FLAGS.data_dir,
        vocab_model_file=FLAGS.vocab_model_file,
        max_encoder_length=FLAGS.max_encoder_length,
        max_decoder_length=FLAGS.max_decoder_length,
        substitute_newline=FLAGS.substitute_newline,
        is_training=False)
eval_dataset = eval_input_fn({'batch_size': 8})
```

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

### Load Model Checkpoint

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Restores model weights from a specified checkpoint path for continued training or inference.

```python
ckpt_path = 'gs://bigbird-transformer/summarization/pubmed/roberta/model.ckpt-300000'
ckpt_reader = tf.compat.v1.train.NewCheckpointReader(ckpt_path)
loaded_weights = []
for v in tqdm(model.trainable_weights, position=0):
  try:
    val = ckpt_reader.get_tensor(v.name[:-2])
  except:
    val = v.numpy()
  loaded_weights.append(val)

model.set_weights(loaded_weights)
```

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

### Configure and Use TransformerModel for Seq2Seq Tasks

Source: https://context7.com/google-research/bigbird/llms.txt

This snippet demonstrates setting up and using the TransformerModel for sequence-to-sequence tasks, featuring BigBird attention in the encoder and beam search for generation. Note the 'norm_type' for Pegasus-style pre-normalization and parameters for beam search decoding.

```python
from bigbird.core import modeling
from bigbird.core import utils
import tensorflow as tf

# Configure seq2seq parameters
params = utils.get_default_config()
params['vocab_size'] = 32000
params['hidden_size'] = 768
params['num_hidden_layers'] = 12
params['num_attention_heads'] = 12
params['intermediate_size'] = 3072
params['max_encoder_length'] = 4096
params['max_decoder_length'] = 256
params['attention_type'] = 'block_sparse'
params['norm_type'] = 'prenorm'  # Pegasus-style pre-normalization
params['beam_size'] = 5
params['alpha'] = 0.8  # Length penalty
params['scope'] = 'pegasus'

# Create encoder-decoder model
model = modeling.TransformerModel(params)

# Prepare inputs
input_ids = tf.constant([[1, 2, 3, 4, 5] + [0] * 4091])  # Source document
target_ids = tf.constant([[2, 3, 4, 1]])  # Target summary (for training)

# Training forward pass
(log_probs, logits, pred_ids), encoder_output = model(
    input_ids=input_ids,
    target_ids=target_ids,
    training=True
)
# log_probs: [batch, target_length] - log probabilities of target tokens
# logits: [batch, target_length, vocab_size] - raw logits
# pred_ids: [batch, target_length] - predicted token ids

# Inference (beam search generation)
(_, _, generated_ids), _ = model(
    input_ids=input_ids,
    target_ids=None,  # No targets during inference
    training=False
)
```

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

### Configure Model Parameters

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Sets the configuration flags for the BigBird model, including data directory, attention type, and training hyperparameters.

```python
FLAGS.data_dir = "tfds://imdb_reviews/plain_text"
FLAGS.attention_type = "block_sparse"
FLAGS.max_encoder_length = 4096  # reduce for quicker demo on free colab
FLAGS.learning_rate = 1e-5
FLAGS.num_train_steps = 2000
FLAGS.attention_probs_dropout_prob = 0.0
FLAGS.hidden_dropout_prob = 0.0
FLAGS.use_gradient_checkpointing = True
FLAGS.vocab_model_file = "gpt2"
```

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

### Load Summarization Model

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Loads a pre-trained BigBird summarization model from a specified Google Cloud Storage path. This model is intended for serving.

```python
path = 'gs://bigbird-transformer/summarization/pubmed/roberta/saved_model'
imported_model = tf.saved_model.load(path, tags='serve')
summerize = imported_model.signatures['serving_default']
```

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

### Import ROUGE evaluation modules

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Required imports for calculating ROUGE scores using the rouge_score library.

```python
from rouge_score import rouge_scorer
from rouge_score import scoring
```

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

### Run Model Evaluation

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Iterates through the evaluation dataset to compute and print final loss and accuracy metrics.

```python
eval_loss = tf.keras.metrics.Mean(name='eval_loss')
eval_accuracy = tf.keras.metrics.CategoricalAccuracy(name='eval_accuracy')

for ex in tqdm(eval_dataset, position=0):
  loss, log_probs = fwd_only(ex[0], ex[1])
  eval_loss(loss)
  eval_accuracy(tf.one_hot(ex[1], 2), log_probs)
print('Loss = {}  Accuracy = {}'.format(eval_loss.result().numpy(), eval_accuracy.result().numpy()))
```

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

### Run Evaluation with ROUGE Scoring

Source: https://context7.com/google-research/bigbird/llms.txt

Executes the summarization evaluation script using the specified dataset and model parameters.

```bash
python -m bigbird.summarization.run_summarization \
    --data_dir="tfds://scientific_papers/pubmed" \
    --output_dir="/tmp/bigbird_pubmed" \
    --max_encoder_length=4096 \
    --max_decoder_length=256 \
    --do_eval=True
```

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

### Evaluation Loop

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Iterates through the evaluation dataset to calculate log-likelihood metrics.

```python
eval_llh = tf.keras.metrics.Mean(name='eval_llh')

for ex in tqdm(eval_dataset, position=0):
  llh, logits, pred_ids = fwd_only(ex[0], ex[1])
  eval_llh(llh)
print('Log Likelihood = {}'.format(eval_llh.result().numpy()))
```

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

### Configure Model Parameters

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Set hyperparameters for the BigBird model, including attention type and sequence lengths.

```python
FLAGS.data_dir = "tfds://scientific_papers/pubmed"
FLAGS.attention_type = "block_sparse"
FLAGS.couple_encoder_decoder = True
FLAGS.max_encoder_length = 3072  # reduce for quicker demo on free colab
FLAGS.max_decoder_length = 256
FLAGS.block_size = 64
FLAGS.learning_rate = 1e-5
FLAGS.num_train_steps = 10000
FLAGS.attention_probs_dropout_prob = 0.0
FLAGS.hidden_dropout_prob = 0.0
FLAGS.use_gradient_checkpointing = True
FLAGS.vocab_model_file = "gpt2"
```

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

### Configure and Use BertModel with BigBird Attention

Source: https://context7.com/google-research/bigbird/llms.txt

Use this snippet to configure and run a BERT-style encoder model with BigBird's block sparse attention for long sequences. Ensure 'block_sparse' attention type is set for sequences longer than 512 tokens.

```python
from bigbird.core import modeling
from bigbird.core import utils
import tensorflow as tf

# Configure BigBird parameters
params = utils.get_default_config()
params['vocab_size'] = 32000
params['hidden_size'] = 768
params['num_hidden_layers'] = 12
params['num_attention_heads'] = 12
params['intermediate_size'] = 3072
params['max_encoder_length'] = 4096  # Long sequence support
params['attention_type'] = 'block_sparse'  # Options: 'original_full', 'simulated_sparse', 'block_sparse'
params['block_size'] = 64
params['num_rand_blocks'] = 3
params['scope'] = 'bert'

# Create the model
model = modeling.BertModel(params)

# Prepare input (batch_size=2, seq_length=4096)
input_ids = tf.constant([[101, 2054, 2003, 1996] + [0] * 4092,
                         [101, 1045, 2293, 2017] + [0] * 4092])
token_type_ids = tf.zeros_like(input_ids, dtype=tf.int32)

# Forward pass
sequence_output, pooled_output = model(
    input_ids=input_ids,
    token_type_ids=token_type_ids,
    training=False
)
# sequence_output: [batch_size, seq_length, hidden_size] - contextualized representations
# pooled_output: [batch_size, hidden_size] - [CLS] token representation for classification
```

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

### Load PubMed Dataset

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Loads the 'scientific_papers/pubmed' dataset from TensorFlow Datasets for testing. The dataset is loaded without shuffling.

```python
dataset = tfds.load('scientific_papers/pubmed', split='test', shuffle_files=False, as_supervised=True)
```

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

### Compute scores over a dataset

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/eval.ipynb

Iterate through a dataset to generate summaries and aggregate their ROUGE scores.

```python
for ex in tqdm(dataset.take(100), position=0):
  predicted_summary = summerize(ex[0])['pred_sent'][0]
  score = scorer.score(ex[1].numpy().decode('utf-8'), predicted_summary.numpy().decode('utf-8'))
  aggregator.add_scores(score)
```

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

### Perform Inference

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Runs a single forward pass to obtain predicted IDs for a given input.

```python
_, _, pred_ids = fwd_only(ex[0], ex[1])
```

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

### Big Bird Citation

Source: https://github.com/google-research/bigbird/blob/master/README.md

Cite this paper if you find the Big Bird model useful for your research.

```bibtex
@article{zaheer2020bigbird,
  title={Big bird: Transformers for longer sequences},
  author={Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}
```

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

### Define Forward and Backward Pass

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Define a TensorFlow function for the forward and backward pass, including loss calculation and gradient computation.

```python
@tf.function(experimental_compile=True)
def fwd_bwd(features, labels):
  with tf.GradientTape() as g:
    (llh, logits, pred_ids), _ = model(features, target_ids=labels,
                                       training=True)
    loss = run_summarization.padded_cross_entropy_loss(
        logits, labels,
        transformer_config["label_smoothing"],
        transformer_config["vocab_size"])
  grads = g.gradient(loss, model.trainable_weights)
  return loss, llh, logits, pred_ids, grads
```

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

### Define Forward and Backward Pass

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

Defines a TensorFlow function to compute the loss and gradients for the model.

```python
@tf.function(experimental_compile=True)
def fwd_bwd(features, labels):
  with tf.GradientTape() as g:
    _, pooled_output = model(features, training=True)
    loss, log_probs = headl(pooled_output, labels, True)
  grads = g.gradient(loss, model.trainable_weights+headl.trainable_weights)
  return loss, log_probs, grads
```

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

### Define Attention Type Flag

Source: https://github.com/google-research/bigbird/blob/master/README.md

Defines an enum flag for selecting the attention implementation. 'block_sparse' enables BigBird's attention module. Consider 'original_full' for sequence lengths less than 1024.

```python
flags.DEFINE_enum(
    "attention_type", "block_sparse",
    ["original_full", "simulated_sparse", "block_sparse"],
    "Selecting attention implementation. "
    "'original_full': full attention from original bert. "
    "'simulated_sparse': simulated sparse attention. "
    "'block_sparse': blocked implementation of sparse attention.")
```

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

### Define Forward-Only Pass

Source: https://github.com/google-research/bigbird/blob/master/bigbird/classifier/imdb.ipynb

A compiled TensorFlow function for inference or evaluation without updating model weights.

```python
@tf.function(experimental_compile=True)
def fwd_only(features, labels):
  _, pooled_output = model(features, training=False)
  loss, log_probs = headl(pooled_output, labels, False)
  return loss, log_probs
```

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

### Forward Pass Function

Source: https://github.com/google-research/bigbird/blob/master/bigbird/summarization/pubmed.ipynb

Defines a compiled forward pass for inference or evaluation without gradient calculation.

```python
@tf.function(experimental_compile=True)
def fwd_only(features, labels):
  (llh, logits, pred_ids), _ = model(features, target_ids=labels,
                                       training=False)
  return llh, logits, pred_ids
```

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