### Start Model Training

Source: https://github.com/ereverter/bertsum-hf/blob/main/README.md

Use this command to initiate the model training process. Specify input data, output directories for preprocessed data and checkpoints, and the configuration file.

```bash
python run_extsum.py \
    -i data/ext_cnn_dailymail \
    -o data/prep_ext_cnn_dailymail \
    -c config.json \
    -d checkpoints/bertsum/
```

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

### Load Dataset, Tokenizer, and Model

Source: https://github.com/ereverter/bertsum-hf/blob/main/README.md

Use Hugging Face's `datasets`, `AutoTokenizer`, and `AutoModel` to load the extractive CNN Daily Mail dataset and the fine-tuned BERT model. Ensure you have the necessary libraries installed.

```python
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModel

dataset = load_dataset("ereverter/cnn_dailymail_extractive", "3.0.0")
tokenizer = AutoTokenizer.from_pretrained("ereverter/bert-finetuned-cnn_dailymail")
model = AutoModel.from_pretrained("ereverter/bert-finetuned-cnn_dailymail")
```

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

### Prepare Model Inputs

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Prepares sample data for model input using the tokenizer. It then separates the updated sample from the model inputs.

```python
model_inputs = prepare_sample(sample, tokenizer)
updated_sample = model_inputs.pop('sample')
```

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

### Prepare Sample for Summarization

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Selects a sample from the loaded dataset for processing. This snippet retrieves the source text of the 24th sample from the test split of the dataset.

```python
sample = data_dict['test'][24]['src']
sample
```

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

### Evaluate Model Performance

Source: https://github.com/ereverter/bertsum-hf/blob/main/README.md

Execute this command to evaluate the trained model using the ROUGE metric. Provide the path to the model checkpoint, the dataset for evaluation, and directories for saving results.

```bash
python evaluate_model.py \
  --model checkpoints/bertsum/bertsum \
  --dataset data/ext_cnn_dailymail \
  --save_dir checkpoints/bertsum \
  --save_name results.json
```

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

### Load Tokenizer and Model

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Loads the tokenizer and the BertSummarizer model from a pre-trained checkpoint. This is a prerequisite for processing text data.

```python
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = BertSummarizer.from_pretrained(checkpoint)
```

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

### Perform Model Inference

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Runs the model with the prepared inputs and captures the outputs, which include logits and attention masks.

```python
outputs = model(**model_inputs)
outputs
```

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

### Import Libraries and Define Constants

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Imports necessary libraries such as numpy and components from the BertSummarizer and Hugging Face transformers libraries. It also defines the model checkpoint and dataset names.

```python
import numpy as np
from src.bertsum import BertSummarizerConfig, BertSummarizer
from transformers import AutoTokenizer, AutoModel
from datasets import load_dataset
from utils import tokenize_text_to_sentences, prepare_sample

checkpoint = 'eReverter/bert-finetuned-cnn_dailymail'
dataset = 'eReverter/cnn_dailymail_extractive'
```

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

### Load Dataset

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Loads the specified dataset from Hugging Face Datasets. This snippet shows how to load the dataset and then display its structure.

```python
data_dict = load_dataset('eReverter/cnn_dailymail_extractive')
data_dict
```

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

### Generate Summary from Model Output

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Selects the top 3 sentences based on the model's logits to form a summary. This requires the model outputs and the preprocessed sample.

```python
# Select top 3 sentences for the summary
summary = ' '.join([updated_sample[i] for i in outputs['logits'].topk(3).indices.detach().cpu().numpy()[0]])
summary
```

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

### Convert Abstractive to Extractive Dataset

Source: https://github.com/ereverter/bertsum-hf/blob/main/README.md

Use this command to convert abstractive summarization datasets to an extractive format. Specify dataset path, HuggingFace hub status, field names for text and summary, desired summary length, selection algorithm, and output path.

```bash
python abs_to_ext.py \
    -f cnn_dailymail \
    -c '3.0.0' \
    -hf \
    -s 'article' \
    -t 'highlights' \
    -sz 3 \
    -m 'greedy' \
    -o data/ext_cnn_dailymail
```

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

### Text Summarization Pipeline

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

This snippet demonstrates a complete text summarization process using Hugging Face Transformers. It tokenizes input text, prepares it for the model, generates a summary, and extracts the top 3 most relevant sentences.

```python
wikipedia_text = ""
Wine is an alcoholic drink typically made from fermented grapes. Yeast consumes the sugar in the grapes and converts it to ethanol and carbon dioxide, releasing heat in the process. Different varieties of grapes and strains of yeasts are major factors in different styles of wine. These differences result from the complex interactions between the biochemical development of the grape, the reactions involved in fermentation, the grape's growing environment (terroir), and the wine production process. Many countries enact legal appellations intended to define styles and qualities of wine. These typically restrict the geographical origin and permitted varieties of grapes, as well as other aspects of wine production. Wines can be made by fermentation of other fruit crops such as plum, cherry, pomegranate, blueberry, currant and elderberry.
"

sample = tokenize_text_to_sentences(wikipedia_text)
model_inputs = prepare_sample(sample, tokenizer)
updated_sample = model_inputs.pop('sample')
outputs = model(**model_inputs)
summary = ' '.join([updated_sample[i] for i in np.sort(outputs['logits'].topk(3).indices.detach().cpu().numpy()[0])])
summary
```

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

### Verify Output Length

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Checks if the number of logits generated by the model matches the length of the updated sample.

```python
len(outputs['logits'][0]) == len(updated_sample)
```

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

### Load Autoreload Extension

Source: https://github.com/ereverter/bertsum-hf/blob/main/playground.ipynb

Loads the autoreload extension for interactive Python environments. This extension automatically reloads modules before executing code, which is useful during development.

```python
%load_ext autoreload
%autoreload 2
```

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