alea-institute
/

kl3m-multi-word-002-128k

@@ -1,199 +1,301 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+language:
+- en
+license: mit
+tags:
+- tokenizer
+- legal
+- bpe
+- byte-pair-encoding
+- multi-word
+- kl3m
+- legal-domain
+- hierarchical
+pipeline_tag: fill-mask
 library_name: transformers
 ---
+# KL3M Multi-Word Tokenizer v2 - 128K
+This is the **131,072 token** variant of the KL3M (Kelvin Legal Large Language Model) multi-word tokenizer family v2, optimized for legal domain text with hierarchical vocabulary nesting.
+## Overview
+The KL3M multi-word tokenizers v2 are an improved family of byte-pair encoding (BPE) tokenizers trained on ~44GB of legal domain text from the [KL3M dataset](https://aleainstitute.ai/work/kl3m/) (copyright-clean legal corpus from the ALEA Institute). These tokenizers:
+- **Capture multi-word legal phrases** as single tokens (e.g., "Licensee", "hereinafter", "indemnification")
+- **Use hierarchical vocabulary nesting** where smaller vocabularies are proper subsets of larger ones
+- **Outperform GPT-4 on legal text** with 7.5% better compression on legal documents
+- **Enable vocabulary expansion experiments** and transfer learning across vocabulary sizes
+## What's New in v2
+- **Cleaner special token design**: 7 special tokens (removed experimental symbols)
+- **Improved legal domain optimization**: Better encoding of common legal terminology
+- **Superior compression**: 5.32 chars/token on legal text (vs 4.92 for GPT-4)
+- **Smaller file sizes**: More efficient tokenizer representation
+## Performance Comparison
+On a realistic 3,743-character legal document (Software License Agreement):
+| Tokenizer | Vocab Size | Tokens | Chars/Token | vs GPT-4 |
+|-----------|------------|--------|-------------|----------|
+| **KL3M v2-128K** | 131,072 | **704** | **5.32** | **-7.5%** |
+| GPT-4o/5 | 200,019 | 757 | 4.94 | +0.5% |
+| GPT-4 | 100,277 | 761 | 4.92 | baseline |
+| GPT-2 | 50,257 | 858 | 4.36 | +12.7% |
+| KL3M v2-64K | 65,536 | 802 | 4.67 | +5.4% |
+| KL3M v2-32K | 32,768 | 943 | 3.97 | +23.9% |
+### Legal Terminology Efficiency
+Common legal terms as single tokens (128K vocab):
+| Term | KL3M v2-128K | GPT-4 | GPT-4o/5 |
+|------|--------------|-------|----------|
+| "Licensee" | 1 token | 2 tokens | 2 tokens |
+| "hereinafter" | 1 token | 3 tokens | 3 tokens |
+| "indemnification" | 1 token | 4 tokens | 3 tokens |
+| "arbitration" | 1 token | 3 tokens | 3 tokens |
+| "WHEREAS" | 1 token | 2 tokens | 2 tokens |
+| "non-exclusive" | 1 token | 2 tokens | 2 tokens |
+## Tokenizer Family
+This tokenizer is part of a hierarchically nested family. Token IDs in smaller vocabularies are **identical** across all larger vocabularies, enabling seamless vocabulary expansion:
+| Vocabulary Size | HuggingFace Repository | File Size |
+|----------------|------------------------|-----------|
+| 4,096 (4K) | [alea-institute/kl3m-multi-word-002-4k](https://huggingface.co/alea-institute/kl3m-multi-word-002-4k) | 248 KB |
+| 8,192 (8K) | [alea-institute/kl3m-multi-word-002-8k](https://huggingface.co/alea-institute/kl3m-multi-word-002-8k) | 516 KB |
+| 16,384 (16K) | [alea-institute/kl3m-multi-word-002-16k](https://huggingface.co/alea-institute/kl3m-multi-word-002-16k) | 1.1 MB |
+| 32,768 (32K) | [alea-institute/kl3m-multi-word-002-32k](https://huggingface.co/alea-institute/kl3m-multi-word-002-32k) | 2.1 MB |
+| 65,536 (64K) | [alea-institute/kl3m-multi-word-002-64k](https://huggingface.co/alea-institute/kl3m-multi-word-002-64k) | 4.4 MB |
+| 131,072 (128K) | [alea-institute/kl3m-multi-word-002-128k](https://huggingface.co/alea-institute/kl3m-multi-word-002-128k) | 8.9 MB |
+**→ You are viewing: 131,072 (128K)**
+## Key Features
+### 1. Multi-Word Tokenization
+Legal text contains frequent multi-word phrases that benefit from being treated as single tokens. The larger vocabularies capture increasingly sophisticated legal terminology:
+**4K vocabulary examples:**
+- Common legal particles: "herein", "thereof", "pursuant"
+- Basic legal terms: "shall", "party", "agreement"
+**32K vocabulary examples:**
+- Complex terms: "Licensee", "Licensor", "jurisdiction", "arbitration"
+- Multi-word phrases: "intellectual property", "force majeure"
+**128K vocabulary examples:**
+- Specialized terms: "indemnification", "confidentiality", "non-exclusive"
+- Complete phrases: "representations and warranties", "WHEREAS"
+### 2. Hierarchical Vocabulary Nesting
+Token IDs 0-4,095 are **identical** across all tokenizer sizes. This enables:
+- **Vocabulary expansion during training**: Start with 4K vocab, expand to 32K mid-training
+- **Transfer learning**: Initialize larger vocab models from smaller vocab checkpoints
+- **Controlled ablations**: Compare vocab sizes while maintaining token alignment
+- **Model compression**: Train with large vocab, deploy with smaller vocab
+### 3. Legal Domain Optimization
+Trained on the KL3M corpus (44GB of legal text):
+- Court opinions and case law
+- Contracts and agreements
+- Patents and IP documents
+- Legal briefs and filings
+- Statutory and regulatory text
+This specialized training produces:
+- **Better compression** on legal documents (5.32 chars/token vs 4.92 for GPT-4)
+- **Semantic coherence** for legal multi-word expressions
+- **Reduced sequence lengths** leading to faster inference
+### 4. Special Tokens (v2)
+Seven essential special tokens for language model training:
+| Token | ID | Purpose |
+|-------|----|---------  |
+| `<\|start\|>` | 0 | Start of sequence |
+| `<\|end\|>` | 1 | End of sequence |
+| `<\|pad\|>` | 2 | Padding token |
+| `<\|unk\|>` | 3 | Unknown token |
+| `<\|cls\|>` | 4 | Classification (BERT) |
+| `<\|sep\|>` | 5 | Separator (BERT) |
+| `<\|mask\|>` | 6 | Mask token (MLM) |
+*Note: v2 removes experimental symbols (⧈, ⚖, ⏵) from v1 for cleaner design.*
+## Usage
+### With Transformers
+```python
+from transformers import PreTrainedTokenizerFast
+# Load tokenizer
+tokenizer = PreTrainedTokenizerFast.from_pretrained(
+    "alea-institute/kl3m-multi-word-002-128k"
+)
+# Tokenize text
+text = "The Licensor hereby grants to Licensee a non-exclusive license."
+tokens = tokenizer.encode(text)
+print(f"Tokens: {len(tokens)}")
+# Decode
+decoded = tokenizer.decode(tokens)
+print(f"Decoded: {decoded}")
+```
+### With tokenizers Library
+```python
+from tokenizers import Tokenizer
+# Load tokenizer
+tokenizer = Tokenizer.from_pretrained(
+    "alea-institute/kl3m-multi-word-002-128k"
+)
+# Encode
+encoding = tokenizer.encode(text)
+print(f"Tokens: {encoding.tokens}")
+print(f"IDs: {encoding.ids}")
+```
+### Training a Model
+```python
+from transformers import AutoConfig, AutoModelForMaskedLM, PreTrainedTokenizerFast
+# Load tokenizer
+tokenizer = PreTrainedTokenizerFast.from_pretrained(
+    "alea-institute/kl3m-multi-word-002-128k"
+)
+# Create model config
+config = AutoConfig.from_pretrained(
+    "bert-base-uncased",
+    vocab_size=tokenizer.vocab_size,
+)
+# Initialize model
+model = AutoModelForMaskedLM.from_config(config)
+# Train with HuggingFace Trainer...
+```
+## Technical Details
+### Training Corpus
+- **Source**: KL3M (Kelvin Legal Large Language Model) dataset
+- **Size**: ~44.2 GB (44,168,540,153 bytes)
+- **Lines**: 1,018,355,750
+- **Words**: 5,997,814,602
+- **Domain**: Legal documents (copyright-clean)
+### Training Parameters
+```bash
+bbpe train \
+  --max-entropy 7.0 \
+  --preprocessor unicode-whitespace \
+  --preprocessor-probability 0.1 \
+  --vocab-size 131072 \
+  --family-size 65536 --family-size 32768 \
+  --family-size 16384 --family-size 8192 --family-size 4096
+```
+- **Max entropy**: 7.0 (balances multi-word phrases with common tokens)
+- **Preprocessing**: Unicode whitespace normalization (10% probability)
+- **Byte fallback**: Enabled (handles any input)
+### Vocabulary Structure
+- **Base vocabulary**: 256 bytes + 49 extended chars = 305 base tokens
+- **Learned merges**: vocab_size - 305 - 7 (special tokens)
+- **Nesting property**: All tokens in size N exist in size 2N
+## Recommendations
+### By Use Case
+**Legal Document Processing** (contracts, patents, briefs):
+- **Best**: 128K or 64K vocab
+- **Rationale**: Maximum compression of legal terminology
+- **Benefit**: Shorter sequences, faster inference
+**Resource-Constrained Environments**:
+- **Best**: 16K or 32K vocab
+- **Rationale**: Good balance of compression and model size
+- **Benefit**: Smaller embedding layers, less memory
+**Experimentation / Research**:
+- **Best**: Multiple sizes with vocabulary expansion
+- **Rationale**: Leverage nested structure for novel training strategies
+- **Benefit**: Test curriculum learning, transfer learning
+### Model Size Guidelines
+Choose vocab size based on your model parameter count:
+| Model Size | Recommended Vocab | Embedding Parameters |
+|------------|-------------------|----------------------|
+| <200M params | 4K-8K | 3-6M |
+| 200M-500M params | 8K-16K | 6-13M |
+| 500M-1B params | 16K-32K | 13-26M |
+| 1B-3B params | 32K-64K | 26-52M |
+| >3B params | 64K-128K | 52-104M |
+## Limitations
+- **Training domain**: Optimized for legal English text; may underperform on other domains
+- **Multilingual**: Trained primarily on English; limited non-English support
+- **Code**: Less optimized for code compared to code-specific tokenizers
+- **Vocabulary size**: Larger vocabs (64K+) require more embedding memory
+## Citation
+If you use these tokenizers in your research, please cite:
+```bibtex
+@misc{kl3m-multi-word-002,
+  title={KL3M Multi-Word Tokenizers v2: Hierarchically Nested BPE for Legal Domain},
+  author={ALEA Institute},
+  year={2025},
+  publisher={HuggingFace},
+  url={https://huggingface.co/alea-institute/kl3m-multi-word-002-128k}
+}
+```
+## License
+MIT License
+## About ALEA Institute
+The [ALEA Institute](https://aleainstitute.ai) develops open-source tools and datasets for legal AI, including the KL3M corpus and multi-word tokenizers.
+## Related Resources
+- **KL3M Dataset**: [aleainstitute.ai/work/kl3m](https://aleainstitute.ai/work/kl3m/)
+- **bbpe Tokenizer Trainer**: [github.com/microsoft/blingfire](https://github.com/microsoft/blingfire)
+- **v1 Tokenizers**: [alea-institute/kl3m-multi-word-001-*](https://huggingface.co/alea-institute/kl3m-multi-word-001-4k)
+## Version History
+- **v2 (002 series)**: Improved special token design, better legal optimization
+- **v1 (001 series)**: Initial release with 10 special tokens
+## Contact
+For questions or issues, please contact: [contact information TBD]