---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: source
    dtype: string
  splits:
  - name: train
    num_bytes: 107402461357
    num_examples: 431867387
  download_size: 63321627068
  dataset_size: 107402461357
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- da
size_categories:
- 100M<n<1B
license: unknown
---

# Details

**SnakModel** is a 7B-parameter, autoregressive language model specifically designed for Danish. There are both an instruction-tuned variant, as well as a base version for further fine-tuning. Our models build upon [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b-hf), which we continuously pre-train on a diverse collection of Danish corpora.

**Developers**

[**🧭 NLPnorth** research unit](https://nlpnorth.github.io) at the [IT University of Copenhagen](https://itu.dk), Denmark.  
[**🌊 AAU-NLP** research unit](https://aaunlp.github.io) at [Aalborg University Copenhagen](https://aau.dk), Denmark.

[Mike Zhang](https://jjzha.github.io)\*, [Max Müller-Eberstein](https://mxij.me)\*, [Elisa Bassignana](http://elisabassignana.github.io), [Rob van der Goot](https://robvanderg.github.io).  
\*equal contribution.


## Deduplication

**Important Note**: In this data, we removed two sources, namely DaNewsroom and FTSpeech. The reason is because the licenses are not clear.

The hyperparameters of **this** particular pretraining set:

```
  --seed 42 \
  --batch_size 1024 \
  --num_perm 64 \
  --threshold 0.85 \
  --hash_bits 32 \
  --num_proc 16 \
```

The hyperparameters of the **original** pretraining set:

```
  --seed 42 \
  --batch_size 4096 \
  --num_perm 128 \
  --threshold 0.85 \
```

Note how the number of permutations is lower, a lower batch size and we have lower hash bits `64 -> 32`. 
We encountered several OOM errors that were a bit inexplicable and decided to lower the memory footprint in this way.
The hardware we used was a machine with 128 cores and 1TB of RAM. This data should take less than 100GB of disk space.

## Licenses

For licensing of the data, we refer to the paper. Specifically for the Twitter data, we made an assumption that the data has been extracted using a version of the Twitter API which is usually MIT-licensed, to consider it as "an upper-bound". 
Though, we understand that the software/code is usually MIT-licensed. Feel free to leave the Twitter data out. Other Twitter datasets here on HF seem to have a flavor of cc-by*.

## Citation

If you find the work in this repository useful, please don't forget to cite:

```bibtex
@inproceedings{snakmodel,
  title={{S}nak{M}odel: Lessons Learned from Training an Open Danish Large Language Model},
  author={Mike Zhang and Max M{\"u}ller-Eberstein and Elisa Bassignana and Rob van der Goot},
  booktitle={The Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies},
  year={2024},
  url={https://openreview.net/forum?id=YxzfgQGpRQ}
}
```