85 lines
2.3 KiB
Markdown
85 lines
2.3 KiB
Markdown
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---
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language:
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- hu
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- en
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- zh
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tags:
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- text-generation
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- puli
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license: cc-by-nc-4.0
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widget:
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- text: Elmesélek egy történetet a nyelvtechnológiáról.
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---
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# PULI Trio base (7.67B billion parameter)
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For further details read [our paper](http://real.mtak.hu/173960/1/TSD_2023_GPT.pdf) or testing our instruct model, see [our demo site](https://puli.nytud.hu/puli-trio).
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- Hungarian-English-Chinese trilingual GPT-NeoX model (7.67B billion parameter)
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- Trained with EleutherAI's GPT-NeoX [github](https://github.com/EleutherAI/gpt-neox)
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- Checkpoint: 410 000 steps
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## Dataset
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- Hungarian: 41.5 billion words (314 GB)
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- English: 61.9 billion words (391 GB)
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- Github: 6 million documents (33 GB)
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- Chinese: 98.7 billion Chinese character (340 GB)
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- (12 billion non Chinese token)
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## Limitations
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- max_seq_length = 2048
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- float16
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- vocab size: 150 016
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## Citation
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If you use this model, please cite the following paper:
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```
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@inproceedings {yang-puli-gptrio,
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title = {Mono- and multilingual GPT-3 models for Hungarian},
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booktitle = {Text, Speech, and Dialogue},
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year = {2023},
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publisher = {Springer Nature Switzerland},
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series = {Lecture Notes in Computer Science},
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address = {Plzeň, Czech Republic},
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author = {Yang, Zijian Győző and Laki, László János and Váradi, Tamás and Prószéky, Gábor},
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pages = {94--104},
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isbn = {978-3-031-40498-6}
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}
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```
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## Usage
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```python
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from transformers import GPTNeoXForCausalLM, AutoTokenizer
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model = GPTNeoXForCausalLM.from_pretrained("NYTK/PULI-GPTrio")
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tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-GPTrio")
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prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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gen_tokens = model.generate(
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input_ids,
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do_sample=True,
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temperature=0.9,
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max_length=100,
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)
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gen_text = tokenizer.batch_decode(gen_tokens)[0]
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print(gen_text)
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```
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## Usage with pipeline
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```python
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from transformers import pipeline, GPTNeoXForCausalLM, AutoTokenizer
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model = GPTNeoXForCausalLM.from_pretrained("NYTK/PULI-GPTrio")
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tokenizer = AutoTokenizer.from_pretrained("NYTK/PULI-GPTrio")
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prompt = "Elmesélek egy történetet a nyelvtechnológiáról."
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generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
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print(generator(prompt)[0]["generated_text"])
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```
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