language:
- 'no'
- nb
- nn
inference: true
tags:
- gpt
- generative
- bloom
license: cc-by-4.0
pipeline_tag: text-generation
datasets:
- uonlp/CulturaX
- NbAiLab/NCC
- vikp/starcoder_filtered
NorBLOOM-7b-scratch
NorBLOOM-7b-scratch is a large Norwegian language model pretrained from scratch on a total of 260 billion subword tokens (using six repetitions of open Norwegian texts).
This model is a part of the NORA-LLM family developed in collaboration between the Language Technology Group at the University of Oslo, the High Performance Language Technologies (HPLT) project, the National Library of Norway, and the University of Turku. All the models are pre-trained on the same dataset and with the same tokenizer. NorBLOOM-7b-scratch has around 7 billion parameters and is based on the BLOOM architecture.
The NORA-LLM language model family includes (as of now):
- NorMistral-7b-warm -- an LLM initialized from Mistral-7b-v0.1 and continuously pretrained on Norwegian data;
- NorMistral-7b-scratch -- a Mistral-based LLM pretrained from scratch on Norwegian data;
- NorBLOOM-7b-scratch -- a BLOOM-based LLM pretrained from scratch on Norwegian data.
Disclaimer: This model is pretrained on raw (mostly web-based) textual data. It is not finetuned to follow instructions, and it can generate harmful completions after inappropriate user prompts. It is primarily intended for research purposes.
Pretraining corpus
The model is pretrained exclusively on publicly available data. We combine the resources from the public part of the NCC corpus, from the cleaned HPLT corpus, and from CulturaX. This resulted in over 34B subword tokens of Norwegian (Bokmål or Nynorsk) in total, which amounts to about 26.7B whitespace-separated tokens. We also augment the corpus with Starcoder; 20% of the 260B tokens are sampled from this code corpus. The natural language data is repeated six times to get the pretraining budget of 260B tokens, in accordance with findings from Muennighoff et al. (2023).
Model details
Model Developers: Language Technology Group at the University of Oslo.
Input: Textual input.
Output: Generated text.
Model Architecture: NorBLOOM is an auto-regressive language model that uses the BLOOM architecture.
| Training Data | Params | Context Length | Tokens | LR | |
|---|---|---|---|---|---|
| NorMistral-7b-warm | NCC+HPLT+CulturaX+Starcoder | 7B | 2k | 260B | 1.0 x 10-4 |
| NorMistral-7b-scratch | NCC+HPLT+CulturaX+Starcoder | 7B | 2k | 260B | 3.0 x 10-4 |
| NorBLOOM-7b-scratch | NCC+HPLT+CulturaX+Starcoder | 7B | 2k | 260B | 1.2 x 10-4 |
Tokenizer: Byte-based BPE tokenizer trained on the same Norwegian corpus as this model. The vocabulary size is 32,768 tokens.
Training FLOPs The approximate amount is 1.12e+22 FLOPs; calculated as in Chowdhery et al. (2022).
Model Dates: This model was pretrained in December 2023.
Status: This is only a pretrained language model; an instruction-finetuned model will follow soon.
License: Creative Commons Attribution 4.0
Research Paper: Forthcoming
Initial evaluation
Disclaimer: our model evaluation is an ongoing phase and is not claimed to be exhaustive. We provide our initial evaluation results on standard natural language understanding and generation tasks, and our evaluation design will be extended. The user should perform evaluation for their particular model application scenario, including safety and bias evaluations.
The perplexity on the heldout validation set from the Norwegian Colossal Corpus (NCC) is 7.43 and the final training perplexity is 4.76.
Our initial downstream evaluation is conducted on reading comprehension, sentiment analysis and machine translation tasks using open-source peer-reviewed datasets and benchmarks in native Norwegian. We release our codebase here. We compare against other pretrained generative language models that officially support Norwegian: NB-GPT-J, GPT-Sw3 6.7B, GPT-Sw3 6.7B v2, and Falcon-7B; we also include evaluation of Mistral-7b-v0.1.
Sentiment analysis
NoReC (Øvrelid et al., 2020) is a dataset for sentence-level sentiment analysis derived from the Norwegian Review Corpus (Velldal et al., 2018). We use the binary formulation of this task (positive vs. negative).
Method
- Evaluation setting: zero-shot and few-shot perplexity-based evaluation.
- Prompt:
"Tekst: {text}\nSentiment:{label}", where thelabelis either "positiv" or "negativ". - Few-shot results show the average scores across 5 repetitions
- Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/sentiment_analysis.py
- Performance metric: macro-averaged F1-score.
Macro-averaged F1-scores on the sentence-level sentiment analysis task (NoReC)
| Model | 0-shot (macro F1) | 1-shot (macro F1) | 16-shot (macro F1) |
|---|---|---|---|
| NorMistral-7b-warm | 60.6 | 77.8 | 87.3 |
| NorMistral-7b-scratch | 47.3 | 62.2 | 80.1 |
| NorBLOOM-7b | 75.7 | 73.8 | 65.5 |
| NB-GPT-J | 48.4 | 56.5 | 65.2 |
| GPT-Sw3-6.7B | 61.5 | 72.2 | 76.5 |
| GPT-Sw3-6.7B-v2 | 42.4 | 69.1 | 83.4 |
| Falcon-7B | 53.3 | 61.6 | 74.9 |
| Mistral-7B-v0.1 | 70.2 | 72.9 | 84.8 |
Reading comprehension
NorQuAD (Ivanova et al., 2023) is a dataset for extractive question answering in Norwegian designed similarly to SQuAD (Rajpurkar et al., 2016).
Method
- Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
- Prompt:
"Tittel: {title}\n\nTekst: {text}\n\nSpørsmål: {question}\n\nSvar:{answer}"Based on Brown et al. (2020). - Few-shot results show the average scores across 5 repetitions
- Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/norquad.py
- Performance metrics: macro-averaged F1-score and exact match (EM).
Performance results on the extractive question answering task (NorQuAD)
| Model | 0-shot (F1/EM) | 1-shot (F1/EM) | 2-shot (F1/EM) |
|---|---|---|---|
| NorMistral-7b-warm | 48.6/24.8 | 63.6/40.0 | 66.5/43.8 |
| NorMistral-7b-scratch | 34.0/15.7 | 46.5/25.8 | 48.5/27.8 |
| NorBLOOM-7b | 35.0/13.3 | 47.7/28.0 | 49.3/30.1 |
| NB-GPT-J | 24.4/6.8 | 32.8/11.6 | 35.0/12.3 |
| GPT-Sw3-6.7B | 46.5/22.0 | 55.9/32.0 | 58.1/34.3 |
| GPT-Sw3-6.7B-v2 | 46.9/22.5 | 61.1/38.9 | 66.0/44.5 |
| Falcon-7B | 15.8/7.0 | 27.3/13.9 | 27.4/13.1 |
| Mistral-7B-v0.1 | 46.4/22.4 | 64.9/41.1 | 71.7/49.4 |
Machine translation
Tatoeba (Tiedemann, 2020) is a benchmark for machine translation, which includes hundreds of language pairs. We consider six language pairs (English <-> Bokmål, English <-> Nynorsk, and Bokmål <-> Nynorsk).
Method
- Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
- Prompt:
"{source_language}: {source_text}\n{target_language}:{target_text}", where thesource_languageandtarget_languageareEngelsk,Bokmål, orNynorsk. Based on Garcia et al. (2023). - Few-shot results show the average scores across 5 repetitions
- Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/machine_translation.py
- Performance metrics: BLEU (Papineni et al., 2002) and chrF++ (Popović, 2015).
English → Norwegian Bokmål
| Model | 0-shot (BLEU/chrF++) | 1-shot (BLEU/chrF++) | 5-shot (BLEU/chrF++) |
|---|---|---|---|
| NorMistral-7b-warm | 55.8/70.7 | 56.7/71.5 | 57.7/72.4 |
| NorMistral-7b-scratch | 46.4/62.9 | 50.4/66.3 | 52.1/67.6 |
| NorBLOOM-7b | 37.1/53.6 | 50.1/65.8 | 52.0/67.6 |
| NB-GPT-J | 8.6/39.1 | 35.9/64.5 | 47.2/68.7 |
| GPT-Sw3-6.7B | 21.8/55.2 | 54.5/69.6 | 58.6/73.2 |
| GPT-Sw3-6.7B-v2 | 20.6/53.2 | 51.2/66.6 | 58.4/73.0 |
| Falcon-7B | 19.1/40.1 | 20.6/41.8 | 22.1/43.6 |
| Mistral-7B-v0.1 | 32.5/51.9 | 35.4/55.1 | 36.3/56.0 |
English → Norwegian Nynorsk
| Model | 0-shot (BLEU/chrF++) | 1-shot (BLEU/chrF++) | 5-shot (BLEU/chrF++) |
|---|---|---|---|
| NorMistral-7b-warm | 43.6/62.0 | 44.2/63.2 | 44.3/63.7 |
| NorMistral-7b-scratch | 38.0/56.9 | 39.2/57.9 | 40.7/59.3 |
| NorBLOOM-7b | 35.6/54.7 | 36.6/56.3 | 38.1/57.4 |
| NB-GPT-J | 1.7/14.7 | 6.3/34.1 | 35.2/60.4 |
| GPT-Sw3-6.7B | 13.4/44.3 | 43.6/62.5 | 44.5/63.5 |
| GPT-Sw3-6.7B-v2 | 14.8/45.5 | 43.7/62.3 | 44.0/63.6 |
| Falcon-7B | 6.4/28.6 | 8.3/30.5 | 9.3/32.1 |
| Mistral-7B-v0.1 | 11.6/35.7 | 13.5/38.7 | 15.0/40.0 |
Norwegian Bokmål → English
| Model | 0-shot (BLEU/chrF++) | 1-shot (BLEU/chrF++) | 5-shot (BLEU/chrF++) |
|---|---|---|---|
| NorMistral-7b-warm | 55.1/68.4 | 55.5/69.5 | 56.0/69.8 |
| NorMistral-7b-scratch | 47.1/61.9 | 49.4/64.2 | 52.3/66.2 |
| NorBLOOM-7b | 45.0/59.3 | 48.3/64.0 | 49.0/64.7 |
| NB-GPT-J | 9.8/41.4 | 24.8/58.3 | 47.6/67.7 |
| GPT-Sw3-6.7B | 47.8/66.2 | 49.1/68.1 | 49.6/69.4 |
| GPT-Sw3-6.7B-v2 | 46.3/67.5 | 48.9/69.3 | 58.2/72.8 |
| Falcon-7B | 21.6/40.6 | 31.7/47.4 | 36.6/51.7 |
| Mistral-7B-v0.1 | 53.8/68.2 | 54.6/69.0 | 56.9/70.7 |
Norwegian Nynorsk → English
| Model | 0-shot (BLEU/chrF++) | 1-shot (BLEU/chrF++) | 5-shot (BLEU/chrF++) |
|---|---|---|---|
| NorMistral-7b-warm | 55.1/68.4 | 55.5/69.5 | 56.0/69.8 |
| NorMistral-7b-scratch | 47.1/61.9 | 49.4/64.2 | 52.3/66.2 |
| NorBLOOM-7b | 45.0/59.3 | 48.3/64.0 | 49.0/64.7 |
| NB-GPT-J | 2.9/19.5 | 10.1/41.0 | 44.4/66.9 |
| GPT-Sw3-6.7B | 47.8/66.2 | 49.1/68.1 | 49.6/69.4 |
| GPT-Sw3-6.7B-v2 | 46.3/67.5 | 48.9/69.3 | 58.2/72.8 |
| Falcon-7B | 21.6/40.6 | 31.7/47.4 | 36.6/57.1 |
| Mistral-7B-v0.1 | 40.7/57.1 | 46.2/60.7 | 49.9/63.8 |
Norwegian Bokmål → Norwegian Nynorsk
| Model | 0-shot (BLEU/chrF++) | 1-shot (BLEU/chrF++) | 5-shot (BLEU/chrF++) |
|---|---|---|---|
| NorMistral-7b-warm | 75.8/87.5 | 74.0/86.9 | 75.3/87.5 |
| NorMistral-7b-scratch | 38.0/56.9 | 39.2/57.9 | 40.7/59.3 |
| NorBLOOM-7b | 71.5/84.4 | 70.1/84.1 | 71.9/85.1 |
| NB-GPT-J | 6.6/35.5 | 9.6/41.0 | 26.0/64.7 |
| GPT-Sw3-6.7B | 63.6/82.8 | 74.7/86.0 | 75.8/86.9 |
| GPT-Sw3-6.7B-v2 | 57.5/81.1 | 75.3/86.7 | 76.7/87.6 |
| Falcon-7B | 28.7/59.2 | 29.8/60.8 | 32.1/62.3 |
| Mistral-7B-v0.1 | 32.0/62.2 | 32.9/62.6 | 35.2/63.9 |
Norwegian Nynorsk → Norwegian Bokmål
| Model | 0-shot (BLEU/chrF++) | 1-shot (BLEU/chrF++) | 5-shot (BLEU/chrF++) |
|---|---|---|---|
| NorMistral-7b-warm | 88.1/93.6 | 89.2/94.3 | 89.3/94.6 |
| NorMistral-7b-scratch | 85.1/91.4 | 86.6/92.4 | 87.4/93.0 |
| NorBLOOM-7b | 78.7/88.5 | 84.2/90.7 | 87.4/93.0 |
| NB-GPT-J | 2.7/18.5 | 6.9/35.6 | 52.9/84.3 |
| GPT-Sw3-6.7B | 652.3/82.4 | 86.1/92.5 | 87.8/93.6 |
| GPT-Sw3-6.7B-v2 | 72.0/88.6 | 86.1/92.5 | 88.2/93.9 |
| Falcon-7B | 36.7/61.6 | 38.3/63.5 | 45.8/68.1 |
| Mistral-7B-v0.1 | 57.0/74.8 | 59.9/77.5 | 62.6/79.1 |
Hardware and Software
Training Factors: The models were pretrained using the Megatron-DeepSpeed library on the LUMI cluster in Finland.
Carbon Footprint: Pretraining one model took approximately 70k GPU hours of computation on AMD MI250X GPUs (assuming 2 GPUs per one AMD MI250X device), each of which draws 500W. LUMI is one of the most eco-efficient data centers in the world, and its energy consumption is covered 100% with renewable electricity.
Example usage
Let's try to use this model for English-to-Norwegian machine translation using simple zero-shot prompting:
from transformers import AutoTokenizer, AutoModelForCausalLM
# First, we will have to import the tokenizer and the language model
tokenizer = AutoTokenizer.from_pretrained("norallm/norbloom-7b-scratch")
model = AutoModelForCausalLM.from_pretrained("norallm/norbloom-7b-scratch").cuda().eval()
# Now we will define the zero-shot prompt template
prompt = """Engelsk: {0}
Bokmål:"""
# A function that will take care of generating the output
@torch.no_grad()
def generate(text):
text = prompt.format(text)
input_ids = tokenizer(text, return_tensors='pt').input_ids.cuda()
prediction = model.generate(
input_ids,
max_new_tokens=64,
do_sample=False,
eos_token_id=tokenizer('\n').input_ids
)
return tokenizer.decode(prediction[0, input_ids.size(1):]).strip()
# Now you can simply call the generate function with an English text you want to translate:
generate("I'm super excited about this Norwegian NORA model! Can it translate these sentences?")