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sec-bert-base

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  # SEC-BERT
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- <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="sec-bert-logo" width="400"/>
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  SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
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  SEC-BERT consists of the following models:
@@ -60,8 +60,7 @@ model = AutoModel.from_pretrained("nlpaueb/sec-bert-base")
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  | **SEC-BERT-BASE** | increased (0.678), decreased (0.282), declined (0.017), grew (0.016), rose (0.004)
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  | **SEC-BERT-NUM** | increased (0.753), decreased (0.211), grew (0.019), declined (0.010), rose (0.006)
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  | **SEC-BERT-SHAPE** | increased (0.747), decreased (0.214), grew (0.021), declined (0.013), rose (0.002)
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  | Sample | Masked Token |
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  | --------------------------------------------------- | ------------ |
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  | Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018. | billion
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  | **SEC-BERT-BASE** | million (0.972), billion (0.028), millions (0.000), ##million (0.000), m (0.000)
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  | **SEC-BERT-NUM** | million (0.974), billion (0.012), , (0.010), thousand (0.003), m (0.000)
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  | **SEC-BERT-SHAPE** | million (0.978), billion (0.021), % (0.000), , (0.000), millions (0.000)
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-
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  | Sample | Masked Token |
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  | --------------------------------------------------- | ------------ |
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  | Total net sales decreased [MASK]% or $5.4 billion during 2019 compared to 2018. | 2
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  | **SEC-BERT-BASE** | 13 (0.045), 12 (0.040), 11 (0.040), 14 (0.035), 10 (0.035)
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  | **SEC-BERT-NUM** | [NUM] (1.000), one (0.000), five (0.000), three (0.000), seven (0.000)
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  | **SEC-BERT-SHAPE** | [XX] (0.316), [XX.X] (0.253), [X.X] (0.237), [X] (0.188), [X.XX] (0.002)
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  | Sample | Masked Token |
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  | --------------------------------------------------- | ------------ |
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  | Total net sales decreased 2[MASK] or $5.4 billion during 2019 compared to 2018. | %
 
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  # SEC-BERT
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+ <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="SEC-BERT" width="400"/>
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  SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
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  SEC-BERT consists of the following models:
 
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  | **SEC-BERT-BASE** | increased (0.678), decreased (0.282), declined (0.017), grew (0.016), rose (0.004)
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  | **SEC-BERT-NUM** | increased (0.753), decreased (0.211), grew (0.019), declined (0.010), rose (0.006)
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  | **SEC-BERT-SHAPE** | increased (0.747), decreased (0.214), grew (0.021), declined (0.013), rose (0.002)
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+ ---
 
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  | Sample | Masked Token |
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  | --------------------------------------------------- | ------------ |
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  | Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018. | billion
 
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  | **SEC-BERT-BASE** | million (0.972), billion (0.028), millions (0.000), ##million (0.000), m (0.000)
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  | **SEC-BERT-NUM** | million (0.974), billion (0.012), , (0.010), thousand (0.003), m (0.000)
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  | **SEC-BERT-SHAPE** | million (0.978), billion (0.021), % (0.000), , (0.000), millions (0.000)
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+ ---
 
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  | Sample | Masked Token |
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  | --------------------------------------------------- | ------------ |
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  | Total net sales decreased [MASK]% or $5.4 billion during 2019 compared to 2018. | 2
 
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  | **SEC-BERT-BASE** | 13 (0.045), 12 (0.040), 11 (0.040), 14 (0.035), 10 (0.035)
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  | **SEC-BERT-NUM** | [NUM] (1.000), one (0.000), five (0.000), three (0.000), seven (0.000)
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  | **SEC-BERT-SHAPE** | [XX] (0.316), [XX.X] (0.253), [X.X] (0.237), [X] (0.188), [X.XX] (0.002)
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+ ---
 
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  | Sample | Masked Token |
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  | --------------------------------------------------- | ------------ |
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  | Total net sales decreased 2[MASK] or $5.4 billion during 2019 compared to 2018. | %