NanoSocrates/docs/RESOURCES.md

# Resources

## Byte-Pair Encoding (BPE)

### Overview

Byte-Pair Encoding (BPE) is a simple but powerful text compression and tokenization algorithm.
Originally introduced as a data compression method, it has been widely adopted in **Natural Language Processing (NLP)** to build subword vocabularies for models such as GPT and BERT.

---

### Key Idea

BPE works by iteratively replacing the most frequent pair of symbols (initially characters) with a new symbol.
Over time, frequent character sequences (e.g., common morphemes, prefixes, suffixes) are merged into single tokens.

---

### Algorithm Steps

1. **Initialization**
   - Treat each character of the input text as a token.

2. **Find Frequent Pairs**
   - Count all adjacent token pairs in the sequence.

3. **Merge Most Frequent Pair**
   - Replace the most frequent pair with a new symbol not used in the text.

4. **Repeat**
   - Continue until no frequent pairs remain or a desired vocabulary size is reached.

---

### Example

Suppose the data to be encoded is:

```text
aaabdaaabac
```

#### Step 1: Merge `"aa"`

Most frequent pair: `"aa"` → replace with `"Z"`

```text
ZabdZabac
Z = aa
```

---

#### Step 2: Merge `"ab"`

Most frequent pair: `"ab"` → replace with `"Y"`

```text
ZYdZYac
Y = ab
Z = aa
```

---

#### Step 3: Merge `"ZY"`

Most frequent pair: `"ZY"` → replace with `"X"`

```text
XdXac
X = ZY
Y = ab
Z = aa
```

---

At this point, no pairs occur more than once, so the process stops.

---

### Decompression

To recover the original data, replacements are applied in **reverse order**:

```text
XdXac
→ ZYdZYac
→ ZabdZabac
→ aaabdaaabac
```

---

### Advantages

- **Efficient vocabulary building**: reduces the need for massive word lists.
- **Handles rare words**: breaks them into meaningful subword units.
- **Balances character- and word-level tokenization**.

---

### Limitations

- Does not consider linguistic meaning—merges are frequency-based.
- May create tokens that are not linguistically natural.
- Vocabulary is fixed after training.