deliciouscat
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README.md
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# Encoder-Decoder model with DeBERTa decoder
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## pre-trained models
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Encoder: `microsoft/deberta-v3-small`
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Decoder: `deliciouscat/deberta-v3-base-decoder-v0.1
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## Data used
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## Training hparams
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optimizer: AdamW, lr=2.3e-5, betas=(0.875, 0.997)
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## How to use
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```
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from transformers import AutoTokenizer, EncoderDecoderModel
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model = EncoderDecoderModel.from_pretrained("
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tokenizer = AutoTokenizer.from_pretrained("
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```
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## Future work!
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train more scientific data
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fine-tune on keyword extraction task
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---
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datasets:
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- HuggingFaceFW/fineweb
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language:
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- en
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---
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# Encoder-Decoder model with DeBERTa decoder
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## pre-trained models
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- Encoder: `microsoft/deberta-v3-small`
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- Decoder: `deliciouscat/deberta-v3-base-decoder-v0.1` (6 transformer layers, 8 attention heads)
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## Data used
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## Training hparams
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- optimizer: AdamW, lr=2.3e-5, betas=(0.875, 0.997)
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- batch size: 12 (maximal on Colab pro A100 env)
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-> training on denoising objective (BART)
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## How to use
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```
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from transformers import AutoTokenizer, EncoderDecoderModel
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model = EncoderDecoderModel.from_pretrained("deliciouscat/deberta-v3-base-encoder-decoder-v0.2")
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tokenizer = AutoTokenizer.from_pretrained("deliciouscat/deberta-v3-base-encoder-decoder-v0.2")
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```
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## Future work!
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- train more scientific data
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- fine-tune on keyword extraction task
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