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bert-test

explosion-testing

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Input Text→ Output Text
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bert-test-sharded

explosion-testing

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IgBert

Exscientia

Model pretrained on protein and antibody sequences using a masked language modeling (MLM) objective. It was introduced in the paper Large scale paired antibody language models. The model is finetuned from IgBert-unpaired using paired antibody sequences from the Observed Antibody Space. Note that the tokeniser adds a `[CLS]` token at the beginning of each paired sequence, a `[SEP]` token at the end of each paired sequence and pads using the `[PAD]` token. For example a batch containing sequences `V Q L [SEP] E V V`, `Q V [SEP] A L` will be tokenised to `[CLS] V Q L [SEP] E V V [SEP]` and `[CLS] Q V [SEP] A L [SEP] [PAD] [PAD]`.

Input Text→ Output Text
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IgBert_unpaired

Exscientia

Model pretrained on protein and antibody sequences using a masked language modeling (MLM) objective. It was introduced in the paper Large scale paired antibody language models. The model is finetuned from ProtBert-BFD using unpaired antibody sequences from the Observed Antibody Space. Note that the tokeniser adds a `[CLS]` token at the beginning of each sequence, a `[SEP]` token at the end of each sequence and pads using the `[PAD]` token. For example a batch containing sequences `E V V M`, `A L` will be tokenised to `[CLS] E V V M [SEP]` and `[CLS] A L [SEP] [PAD] [PAD]`.

Input Text→ Output Text
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IgT5

Exscientia

Model pretrained on protein and antibody sequences using a masked language modeling (MLM) objective. It was introduced in the paper Large scale paired antibody language models. The model is finetuned from IgT5-unpaired using paired antibody sequences from the Observed Antibody Space. Note that the tokeniser adds a ` ` token at the end of each paired sequence and pads using the ` ` token. For example a batch containing sequences `V Q L E V V`, `Q V A L` will be tokenised to `V Q L E V V ` and `Q V A L `.

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IgT5_unpaired

Exscientia

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A compact DeBERTaV2 masked language model pretrained from scratch on Chinese child-directed and child-produced text. Designed for data-constrained language modeling with a pure-Chinese WordPiece tokenizer. Key findings: - Tokenizer granularity is the primary factor for HANZI track performance (8K vocab >> 32K vocab) - DeBERTaV2 achieves the highest NLU scores (zhoblimp: 69.89) - Radical embedding hurts performance with character-level tokenization

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progress

eyad-silx

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caught-in-4k

eymen160

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