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--- |
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license: apache-2.0 |
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datasets: |
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- oscar-corpus/OSCAR-2109 |
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language: |
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- en |
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- pl |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# B-GPT_en_pl_simultaneous |
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This is a bilingual GPT-2 style model. For the first half of training, this model was trained only on English data. In the second half of training, the model was trained on a 50%-50% mix of English and Polish data. At the end of training, 75% of training data seen by the model is English and 25% is Polish. The tokenizer was trained on the same overall proportions of data as the language model at the final step. |
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## Model details: |
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All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences. |
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For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)! |
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Details for this model specifically: |
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* Architecture: gpt2 |
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* Parameters: 124770816 |
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* Maximum sequence length: 512 tokens |
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* Training tokens: 12B |
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* Vocabulary size: 50000 |
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* Compute cost: ~9 NVIDIA A6000 GPU hours |
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* CO2 Emission: 1.17 kg |
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Training dataset: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) |
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Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000. |
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## Use This Model |
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Load the model: |
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Note: if you do not specify a revision, it will load the final checkpoint of the model. See above for the list of checkpoints. The checkpoint step is the name of the revision. |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_en_pl_simultaneous") |
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model = AutoModel.from_pretrained("catherinearnett/B-GPT_en_pl_simultaneous", revision = "128000") |
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```` |
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Text Generation: |
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``` |
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from transformers import pipeline |
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pipe = pipeline("text-generation", model="catherinearnett/B-GPT_en_pl_simultaneous") |
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pipe("I am a") |
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``` |
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## Citation |
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If you use this model, please cite: |
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``` |
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``` |
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