Hugging Face's logo

goldfish-models
/
bcl_latn_full

Text Generation
Transformers
PyTorch
Bikol
Central Bikol
gpt2
goldfish
text-generation-inference
Inference Endpoints
Model card Files Community
bcl_latn_full
File size: 2,686 Bytes 
7018f65
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
 

---
license: apache-2.0
language:
- bik
- bcl
datasets:
- cis-lmu/Glot500
- legacy-datasets/wikipedia
library_name: transformers
pipeline_tag: text-generation
tags:
- goldfish

---

# bcl_latn_full

Goldfish is a suite of monolingual language models trained for 350 languages.
This model is the <b>Central Bikol</b> (Latin script) model trained on 9MB of data (all our data in the language), after accounting for an estimated byte premium of 1.22; content-matched text in Central Bikol takes on average 1.22x as many UTF-8 bytes to encode as English.
The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs).

Note: bcl_latn is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. Macrolanguage code bik_latn (Bikol) is included in Goldfish. Consider using that model depending on your use case.

All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://github.com/tylerachang/goldfish/blob/main/goldfish_paper_20240815.pdf).

Training code and sample usage: https://github.com/tylerachang/goldfish

Sample usage also in this Google Colab: [link](https://colab.research.google.com/drive/1rHFpnQsyXJ32ONwCosWZ7frjOYjbGCXG?usp=sharing)

## Model details:

To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/model_details.json.
All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
Details for this model specifically:

* Architecture: gpt2
* Parameters: 124770816
* Maximum sequence length: 512 tokens
* Training text data (raw): 11.57MB
* Training text data (byte premium scaled): 9.495MB
* Training tokens: 2638336 (x10 epochs)
* Vocabulary size: 50000
* Compute cost: 1.3456800940032e+16 FLOPs or ~1.3 NVIDIA A6000 GPU hours

Training datasets (percentages prior to deduplication):
* 54.73555%: [Glot500](https://huggingface.co/datasets/cis-lmu/Glot500), including [Wortschatz Leipzig Data](https://wortschatz.uni-leipzig.de/en/download), [Wikipedia Hugging Face](https://huggingface.co/datasets/legacy-datasets/wikipedia)
* 45.26445%: [Wikipedia 2023/08](https://dumps.wikimedia.org/)


## Citation

If you use this model, please cite:

```
@article{chang-etal-2024-goldfish,
  title={Goldfish: Monolingual Language Models for 350 Languages},
  author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
  journal={Preprint},
  year={2024},
}
```