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README.md

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+---
+language:
+- en
+- es
+- fr
+- de
+library_name: tokenizers
+license: cc-by-4.0
+tags:
+- kl3m
+- kl3m-004
+- alea
+- legal
+- financial
+date: '2024-12-30T00:00:00.000Z'
+---
+
+# kl3m-004-char-16k-cased
+
+The `kl3m-004-char-16k-cased` **case-sensitive** tokenizer is a domain-specific **character-based** tokenizer trained 
+on a stratified sample of nearly 2M documents across general, legal, and financial domains from the `kl3m-data` project, 
+including American English, British English, Spanish, German, French, Italian, and other common EU languages.
+
+This tokenizer uses the standard Byte-Pair Encoding (BPE) tokenizer from `tokenizers`/`transformers`, but modifies the 
+training process to restrict the vocabulary to tokens that are at most 3 characters long. Models trained with this tokenizer
+should be able to handle a number of use cases that are otherwise difficult to handle with standard tokenizers, such as
+low-resource spell-checking, OCR correction, whitespace normalization, and other tasks that require a high degree of character-level
+granularity.
+
+## Model Details
+
+### Summary
+
+- **Vocabulary**: 16,384 tokens
+- **Tokenizer type:** BPE with 1-4 character tokens
+- **Special token support:** Both causal and masked language modeling
+- **Language(s) (NLP):** Primarily English, Spanish, German, French, with a small percentage of other EU languages.
+- **Data Sources**: See [`kl3m-data`](https://github.com/alea-institute/kl3m-data) repository.
+- **Developed by:** [ALEA Institute](https://aleainstitute.ai).
+- **License:** [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+For more information about the `kl3m-004` tokenizers, see the [kl3m-004-128k-cased tokenizer](https://huggingface.co/alea-institute/kl3m-004-128k-cased).
+
+#### Special Tokens for both Embedding and Generative Models
+
+For both training and inference efficiency, we intended this tokenizer vocabulary to be
+usable for both embedding and generative models. As such, we included special tokens
+suitable for both causal and masked language modeling tasks.
+
+* `<|start|>`: `0`
+* `<|end|>`: `1`
+* `<|pad|>`: `2`
+* `<|unk|>`: `3`
+* `<|sep|>`: `4`
+* `<|cls|>`: `5`
+* `<|mask|>`: `6`
+
+We also added a number of chat and instruction tokens that were not included in `kl3m-001-32k`, including:
+
+* `<|system|>`: `7`
+* `</|system|>`: `8`
+* `<|user|>`: `9`
+* `</|user|>`: `10`
+* `<|instruction|>`: `11`
+* `</|instruction|>`: `12`
+
+These tokens are identical to those used in the `kl3m-003-64k` tokenizer. 
+
+### Replication
+
+The entire data collection and preprocesing pipeline is being made available, along with
+training data, as part of the [ALEA Institute](https://aleainstitute.ai) [KL3M project](https://aleainstitute.ai/work/kl3m/).
+
+The source code to used to train the tokenizer is available on GitHub at:
+[https://github.com/alea-institute/kl3m-embedding-research](https://github.com/alea-institute/kl3m-embedding-research)
+
+The data pipeline will be available on GitHub and S3 in the near future.
+
+This specific tokenizer was trained using the following command:
+
+```bash
+PYTHONPATH=. poetry run python3 \
+  kl3m_tokenizers/tokenizers/kl3m_004/train_char_tokenizer.py \
+  --min_frequency 1000 \
+  --vocab_size 16384 \
+  --pad2 \
+  --max_chars 4 \
+  sample.20241223173012.jsonl.gz \
+  ./kl3m-004-char-16k-cased/
+```
+
+```text
+Training tokenizer.
+[00:33:12] Pre-processing sequences       █████████████████████████████████████████████████████████████ 1849344  /        0
+[00:33:32] Pre-processing sequences       █████████████████████████████████████████████████████████████ 0        /        0
+[00:00:21] Tokenize words                 █████████████████████████████████████████████████████████████ 20286360 / 20286360
+[00:01:01] Count pairs                    █████████████████████████████████████████████████████████████ 20286360 / 20286360
+[00:12:39] Compute merges                 █████████████████████████████████████████████████████████████ 16036    /    16036
+Adding power-of-2 padding tokens.
+Padded vocab to 16384 tokens.
+Special tokens: 13
+Power-of-2 pad tokens: 13
+Final vocab size: 16384
+Training time: 2863.67 seconds
+Output path: kl3m-004-char-16k-cased
+```
+
+### Uses
+This tokenizer is intended to be used for English, Spanish, German, or French language tasks where 
+character-level details are important, such as OCR correction, spell-checking, or tasks where word boundaries
+are not well-defined.  
+
+For a standard BPE "word" tokenizer with a larger vocabulary size, consider using the `kl3m-004-128k-cased` or 
+`kl3m-004-128k-uncased` tokenizers.
+
+### Recommendations
+The kl3m-004-char-16k-cased tokenizer may be particularly useful when character-level details are important but
+resource constraints are not as severe.  For smaller vocabularies with better resource efficiency, consider using the
+kl3m-004-char-4k-cased or kl3m-004-char-8k-cased tokenizers.
+
+### How to Get Started with the Model
+Use the code below to get started with the model.
+
+```
+from tokenizers import Tokenizer
+
+tokenizer = Tokenizer.from_pretrained('alea-institute/kl3m-004-char-16k-cased')
+```
+
+### Citation
+Tokenizer and dataset publications are pending.
+
+## Contact
+
+For any questions, please contact [ALEA Institute](https://aleainstitute.ai) at [hello@aleainstitute.ai](mailto:hello@aleainstitute.ai) or
+create an issue on this repository or [GitHub](https://github.com/alea-institute/kl3m-embedding-research).
+
+![logo](https://aleainstitute.ai/images/alea-logo-ascii-1x1.png)

tokenizer_config.json

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+{"unk_token": "<|unk|>", "bos_token": "<|start|>", "eos_token": "<|end|>", "pad_token": "<|pad|>", "sep_token": "<|sep|>", "cls_token": "<|cls|>", "mask_token": "<|mask|>", "add_prefix_space": false, "do_lower_case": false, "tokenizer_class": "PreTrainedTokenizerFast"}