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README.md
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model = AutoModel.from_pretrained("nlpaueb/sec-bert-base")
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```
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## About Us
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[AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts.
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model = AutoModel.from_pretrained("nlpaueb/sec-bert-base")
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```
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## Use LEBAL-BERT variants as Language Models
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| Model | Masked token | Predictions |
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| ---------------------------------- | ------------ | ------------ |
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| Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018.
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| **BERT-BASE-UNCASED** | decreased | ('increased', '0.678'), ('decreased', '0.282'), ('declined', '0.017'), ('grew', '0.016'), ('rose', '0.004')
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| **SEC-BERT-BASE** | decreased | ('increased', '0.678'), ('decreased', '0.282'), ('declined', '0.017'), ('grew', '0.016'), ('rose', '0.004')
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| **SEC-BERT-NUM** | decreased | ('increased', '0.678'), ('decreased', '0.282'), ('declined', '0.017'), ('grew', '0.016'), ('rose', '0.004')
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| **SEC-BERT-SHAPE** | decreased | ('increased', '0.678'), ('decreased', '0.282'), ('declined', '0.017'), ('grew', '0.016'), ('rose', '0.004')
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| Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018.
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| **BERT-BASE-UNCASED** | billion | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-BASE** | billion | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-NUM** | billion | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-SHAPE** | billion | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| During 2019, the Company [MASK] $67.1 billion of its common stock and paid dividend equivalents of $14.1 billion.
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| **BERT-BASE-UNCASED** | repurchased | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-BASE** | repurchased | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-NUM** | repurchased | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-SHAPE** | repurchased | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| During 2019, the Company repurchased $67.1 billion of its common [MASK] and paid dividend equivalents of $14.1 billion.
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| **BERT-BASE-UNCASED** | stock | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-BASE** | stock | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-NUM** | stock | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-SHAPE** | stock | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| During 2019, the Company repurchased $67.1 billion of its common stock and paid [MASK] equivalents of $14.1 billion.
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| **BERT-BASE-UNCASED** | dividend | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-BASE** | dividend | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-NUM** | dividend | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-SHAPE** | dividend | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| During 2019, the Company repurchased $67.1 billion of its common stock and paid dividend [MASK] of $14.1 billion.
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| **BERT-BASE-UNCASED** | equivalents | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-BASE** | equivalents | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-NUM** | equivalents | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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| **SEC-BERT-SHAPE** | equivalents | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02')
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## About Us
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[AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts.
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