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yhavinga/gpt-neo-1.3B-dutch
gpt-neo-1.3B-dutch is a text generation model from yhavinga. Use it when you need the model to write or continue text. It is set up for transformers.
A GPT-Neo model trained from scratch on Dutch, with perplexity 16.0 on cleaned Dutch mC4.
Downloads · 30 days
117
1% of all-time downloads
All-time downloads
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1.4B
91.2 GB on disk
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4
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.bin5.4 GB · 33%
How the weights are stored.
F321.3B · 93%
From the Hugging Face model README
A GPT-Neo model trained from scratch on Dutch, with perplexity 16.0 on cleaned Dutch mC4.
You can use this GPT-Neo model directly with a pipeline for text generation.
MODEL_DIR='yhavinga/gpt-neo-1.3B-dutch'
from transformers import pipeline, GPT2Tokenizer, GPTNeoForCausalLM
tokenizer = GPT2Tokenizer.from_pretrained(MODEL_DIR)
model = GPTNeoForCausalLM.from_pretrained(MODEL_DIR)
generator = pipeline('text-generation', model, tokenizer=tokenizer)
generated_text = generator('1 - geel. 2 - groen. 3 -', max_length=60, num_beams=4, no_repeat_ngram_size=3, repetition_penalty=2.0)
"1 - geel. 2 - groen. 3 - rood. 4 - blauw. 5 - bruin. 6 - zwart. 7 - oranje. 8 - roze. 9 - paars. 10 - wit. 11 - grijs. 12 - magenta. 13 - lila. 14 - lichtgroen. 15"
This model was trained on the full configuration (33B tokens) of cleaned Dutch mC4,
which is the original mC4, except
TL;DR: yhavinga/gpt2-medium-dutch is the best model.
a/b in the step-column have been trained to step a of a total of b steps.| model | params | train seq len | ppl | loss | batch size | epochs | steps | optim | lr | duration | config | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| yhavinga/gpt-neo-125M-dutch | gpt neo | 125M | 512 | 20.9 | 3.04 | 128 | 1 | 190000/558608 | adam | 2.4e-3 | 1d 12h | full |
| yhavinga/gpt2-medium-dutch | gpt2 | 345M | 512 | 15.1 | 2.71 | 128 | 1 | 320000/520502 | adam | 8e-4 | 7d 2h | full |
| yhavinga/gpt2-large-dutch | gpt2 | 762M | 512 | 15.1 | 2.72 | 32 | 1 | 1100000/2082009 | adafactor | 3.3e-5 | 8d 15h | large |
| yhavinga/gpt-neo-1.3B-dutch | gpt neo | 1.3B | 512 | 16.0 | 2.77 | 16 | 1 | 960000/3049896 | adafactor | 5e-4 | 7d 11h | full |
This project would not have been possible without compute generously provided by Google through the TPU Research Cloud. The HuggingFace 🤗 ecosystem was also instrumental in most, if not all, parts of the training. The following repositories where helpful in setting up the TPU-VM, and training the models:
Created by Yeb Havinga