### Tokenization with Indic NLP Library

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/00-README.md

Demonstrates basic tokenization using the Indic NLP Library. Ensure the library is installed and the language code is correct.

```python
from indicnlp.tokenize import indic_tokenize

text = "This is a sample sentence."
lang_code = "en"

tokens = indic_tokenize.trivial_tokenize(text, lang_code)
print(tokens)
```

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

### Normalize and Tokenize Text with IndicNLP Library

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/6-libraries-and-tools.md

Utilize the IndicNLP library for text normalization and tokenization in Hindi. The 'indic_nlp' library must be installed.

```python
from indic_nlp.tokenize import sentence_tokenize, word_tokenize
from indic_nlp.normalize import normalize

# Normalize text
text = "हेलो वर्ल्ड।"
normalized = normalize(text, lang='hi')

# Tokenize
sentences = sentence_tokenize(normalized, lang='hi')
tokens = word_tokenize(normalized, lang='hi')
```

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

### Load and Use IndicBERT with Transformers

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/6-libraries-and-tools.md

Load the IndicBERT tokenizer and model from Hugging Face Transformers to process Hindi text. Ensure the 'transformers' library is installed.

```python
from transformers import AutoTokenizer, AutoModel

# Load model
tokenizer = AutoTokenizer.from_pretrained("ai4bharat/IndicBERT")
model = AutoModel.from_pretrained("ai4bharat/IndicBERT")

# Process text
text = "यह एक हिंदी वाक्य है।"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```

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

### Loading a Pre-trained Model with Hugging Face Transformers

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/00-README.md

Shows how to load a pre-trained model from Hugging Face Transformers. This is a common pattern for integrating various NLP models.

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "bert-base-uncased"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

print("Model and tokenizer loaded successfully.")
```

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

### Indic NLP Catalog Project File Structure

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/00-README.md

This listing shows the directory structure and the purpose of each file within the Indic NLP Catalog project's output documentation.

```bash
/output/
	├── 00-README.md (this file)
	├── INDEX.md (navigation guide)
	├── 1-project-overview.md (project scope)
	├── 2-text-corpora-datasets.md (150+ datasets)
	├── 3-linguistic-datasets.md (structured annotations)
	├── 4-models-embeddings.md (100+ pre-trained models)
	├── 5-speech-and-ocr.md (speech + OCR)
	├── 6-libraries-and-tools.md (software tools)
	└── 7-language-specific-resources.md (25+ language index)
```

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

### Script Conversion with Indic NLP Library

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/00-README.md

Illustrates converting text from one Indic script to another using the Indic NLP Library. Ensure the correct source and target script codes are provided.

```python
from indicnlp.transliterate import transliterator

text = "This is a sample sentence."
source_script = "latin"
target_script = "bengali"

converter = transliterator.Transliterator(source_script, target_script, reversed_flag=False)
converted_text = converter.process(text)
print(converted_text)
```

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

### Transliteration with Indic NLP Library

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/00-README.md

Demonstrates transliterating text between scripts using the Indic NLP Library. This is useful for converting between Roman and Indic scripts. Ensure the correct language and script codes are used.

```python
from indicnlp.transliterate import indic_transliterate

text = "This is a sample sentence."
lang_code = "en"

transliterated_text = indic_transliterate.transliterate(text, lang_code, lang_code)
print(transliterated_text)
```

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

### Markdown Format for Resource Entries

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/1-project-overview.md

Use this markdown format to contribute new resource entries to the catalog. Ensure all fields are accurately filled.

```markdown
[Resource Name](URL): Brief description of the dataset/model
- Language coverage: List of languages
- Size: Approximate scale (tokens, sentence pairs, hours, etc.)
- Type: Dataset category
- Access: Link to paper, repository, or download
- Contributor: Person or organization identifying the resource
```

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

### IndicNLP Catalog File Structure

Source: https://github.com/ai4bharat/indicnlp_catalog/blob/master/_autodocs/INDEX.md

This snippet shows the directory structure for the IndicNLP Catalog. Each file corresponds to a specific category of resources.

```text
/output/
├── INDEX.md (this file)
├── 1-project-overview.md (project identity, scope, governance)
├── 2-text-corpora-datasets.md (text datasets: monolingual, parallel, classification, etc.)
├── 3-linguistic-datasets.md (parsing, POS, dependency, NER annotations)
├── 4-models-embeddings.md (pre-trained models, embeddings, transformers)
├── 5-speech-and-ocr.md (speech corpora, ASR data, OCR datasets)
├── 6-libraries-and-tools.md (Python libraries, frameworks, tools)
└── 7-language-specific-resources.md (organized by language with cross-references)
```