Sponsored by PoYo.AI
Toolify Toolify logo
AI Models
sec-bert-base

nlpaueb / sec-bert-base

Run on huggingface.co
huggingface.co
Total runs: 114.5K
24-hour runs: 5.0K
7-day runs: 29.8K
30-day runs: 110.7K
Model's Last Updated: April 28 2022
fill-mask

Introduction of sec-bert-base

Model Details of sec-bert-base

SEC-BERT

SEC-BERT

SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications. SEC-BERT consists of the following models:

  • SEC-BERT-BASE (this model): Same architecture as BERT-BASE trained on financial documents.
  • SEC-BERT-NUM : Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation
  • SEC-BERT-SHAPE : Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'.
Pre-training corpus

The model was pre-trained on 260,773 10-K filings from 1993-2019, publicly available at U.S. Securities and Exchange Commission (SEC)

Pre-training details
  • We created a new vocabulary of 30k subwords by training a BertWordPieceTokenizer from scratch on the pre-training corpus.
  • We trained BERT using the official code provided in Google BERT's GitHub repository .
  • We then used Hugging Face 's Transformers conversion script to convert the TF checkpoint in the desired format in order to be able to load the model in two lines of code for both PyTorch and TF2 users.
  • We release a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters).
  • We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4.
  • We were able to use a single Google Cloud TPU v3-8 provided for free from TensorFlow Research Cloud (TRC) , while also utilizing GCP research credits . Huge thanks to both Google programs for supporting us!
Load Pretrained Model 
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-base")
model = AutoModel.from_pretrained("nlpaueb/sec-bert-base")
Using SEC-BERT variants as Language Models
Sample Masked Token
Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018. decreased
Model Predictions (Probability)
BERT-BASE-UNCASED increased (0.221), were (0.131), are (0.103), rose (0.075), of (0.058)
SEC-BERT-BASE increased (0.678), decreased (0.282), declined (0.017), grew (0.016), rose (0.004)
SEC-BERT-NUM increased (0.753), decreased (0.211), grew (0.019), declined (0.010), rose (0.006)
SEC-BERT-SHAPE increased (0.747), decreased (0.214), grew (0.021), declined (0.013), rose (0.002)
Sample Masked Token
Total net sales decreased 2% or $5.4 [MASK] during 2019 compared to 2018. billion
Model Predictions (Probability)
BERT-BASE-UNCASED billion (0.841), million (0.097), trillion (0.028), ##m (0.015), ##bn (0.006)
SEC-BERT-BASE million (0.972), billion (0.028), millions (0.000), ##million (0.000), m (0.000)
SEC-BERT-NUM million (0.974), billion (0.012), , (0.010), thousand (0.003), m (0.000)
SEC-BERT-SHAPE million (0.978), billion (0.021), % (0.000), , (0.000), millions (0.000)
Sample Masked Token
Total net sales decreased [MASK]% or $5.4 billion during 2019 compared to 2018. 2
Model Predictions (Probability)
BERT-BASE-UNCASED 20 (0.031), 10 (0.030), 6 (0.029), 4 (0.027), 30 (0.027)
SEC-BERT-BASE 13 (0.045), 12 (0.040), 11 (0.040), 14 (0.035), 10 (0.035)
SEC-BERT-NUM [NUM] (1.000), one (0.000), five (0.000), three (0.000), seven (0.000)
SEC-BERT-SHAPE [XX] (0.316), [XX.X] (0.253), [X.X] (0.237), [X] (0.188), [X.XX] (0.002)
Sample Masked Token
Total net sales decreased 2[MASK] or $5.4 billion during 2019 compared to 2018. %
Model Predictions (Probability)
BERT-BASE-UNCASED % (0.795), percent (0.174), ##fold (0.009), billion (0.004), times (0.004)
SEC-BERT-BASE % (0.924), percent (0.076), points (0.000), , (0.000), times (0.000)
SEC-BERT-NUM % (0.882), percent (0.118), million (0.000), units (0.000), bps (0.000)
SEC-BERT-SHAPE % (0.961), percent (0.039), bps (0.000), , (0.000), bcf (0.000)
Sample Masked Token
Total net sales decreased 2% or $[MASK] billion during 2019 compared to 2018. 5.4
Model Predictions (Probability)
BERT-BASE-UNCASED 1 (0.074), 4 (0.045), 3 (0.044), 2 (0.037), 5 (0.034)
SEC-BERT-BASE 1 (0.218), 2 (0.136), 3 (0.078), 4 (0.066), 5 (0.048)
SEC-BERT-NUM [NUM] (1.000), l (0.000), 1 (0.000), - (0.000), 30 (0.000)
SEC-BERT-SHAPE [X.X] (0.787), [X.XX] (0.095), [XX.X] (0.049), [X.XXX] (0.046), [X] (0.013)
Sample Masked Token
Total net sales decreased 2% or $5.4 billion during [MASK] compared to 2018. 2019
Model Predictions (Probability)
BERT-BASE-UNCASED 2017 (0.485), 2018 (0.169), 2016 (0.164), 2015 (0.070), 2014 (0.022)
SEC-BERT-BASE 2019 (0.990), 2017 (0.007), 2018 (0.003), 2020 (0.000), 2015 (0.000)
SEC-BERT-NUM [NUM] (1.000), as (0.000), fiscal (0.000), year (0.000), when (0.000)
SEC-BERT-SHAPE [XXXX] (1.000), as (0.000), year (0.000), periods (0.000), , (0.000)
Sample Masked Token
Total net sales decreased 2% or $5.4 billion during 2019 compared to [MASK]. 2018
Model Predictions (Probability)
BERT-BASE-UNCASED 2017 (0.100), 2016 (0.097), above (0.054), inflation (0.050), previously (0.037)
SEC-BERT-BASE 2018 (0.999), 2019 (0.000), 2017 (0.000), 2016 (0.000), 2014 (0.000)
SEC-BERT-NUM [NUM] (1.000), year (0.000), last (0.000), sales (0.000), fiscal (0.000)
SEC-BERT-SHAPE [XXXX] (1.000), year (0.000), sales (0.000), prior (0.000), years (0.000)
Sample Masked Token
During 2019, the Company [MASK] $67.1 billion of its common stock and paid dividend equivalents of $14.1 billion. repurchased
Model Predictions (Probability)
BERT-BASE-UNCASED held (0.229), sold (0.192), acquired (0.172), owned (0.052), traded (0.033)
SEC-BERT-BASE repurchased (0.913), issued (0.036), purchased (0.029), redeemed (0.010), sold (0.003)
SEC-BERT-NUM repurchased (0.917), purchased (0.054), reacquired (0.013), issued (0.005), acquired (0.003)
SEC-BERT-SHAPE repurchased (0.902), purchased (0.068), issued (0.010), reacquired (0.008), redeemed (0.006)
Sample Masked Token
During 2019, the Company repurchased $67.1 billion of its common [MASK] and paid dividend equivalents of $14.1 billion. stock
Model Predictions (Probability)
BERT-BASE-UNCASED stock (0.835), assets (0.039), equity (0.025), debt (0.021), bonds (0.017)
SEC-BERT-BASE stock (0.857), shares (0.135), equity (0.004), units (0.002), securities (0.000)
SEC-BERT-NUM stock (0.842), shares (0.157), equity (0.000), securities (0.000), units (0.000)
SEC-BERT-SHAPE stock (0.888), shares (0.109), equity (0.001), securities (0.001), stocks (0.000)
Sample Masked Token
During 2019, the Company repurchased $67.1 billion of its common stock and paid [MASK] equivalents of $14.1 billion. dividend
Model Predictions (Probability)
BERT-BASE-UNCASED cash (0.276), net (0.128), annual (0.083), the (0.040), debt (0.027)
SEC-BERT-BASE dividend (0.890), cash (0.018), dividends (0.016), share (0.013), tax (0.010)
SEC-BERT-NUM dividend (0.735), cash (0.115), share (0.087), tax (0.025), stock (0.013)
SEC-BERT-SHAPE dividend (0.655), cash (0.248), dividends (0.042), share (0.019), out (0.003)
Sample Masked Token
During 2019, the Company repurchased $67.1 billion of its common stock and paid dividend [MASK] of $14.1 billion. equivalents
Model Predictions (Probability)
BERT-BASE-UNCASED revenue (0.085), earnings (0.078), rates (0.065), amounts (0.064), proceeds (0.062)
SEC-BERT-BASE payments (0.790), distributions (0.087), equivalents (0.068), cash (0.013), amounts (0.004)
SEC-BERT-NUM payments (0.845), equivalents (0.097), distributions (0.024), increases (0.005), dividends (0.004)
SEC-BERT-SHAPE payments (0.784), equivalents (0.093), distributions (0.043), dividends (0.015), requirements (0.009)
Publication

If you use this model cite the following article:
FiNER: Financial Numeric Entity Recognition for XBRL Tagging
Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos and George Paliouras
In the Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022) (Long Papers), Dublin, Republic of Ireland, May 22 - 27, 2022 

@inproceedings{loukas-etal-2022-finer,
    title = {FiNER: Financial Numeric Entity Recognition for XBRL Tagging},
    author = {Loukas, Lefteris and
      Fergadiotis, Manos and
      Chalkidis, Ilias and
      Spyropoulou, Eirini and
      Malakasiotis, Prodromos and
      Androutsopoulos, Ion and
      Paliouras George},
    booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)},
    publisher = {Association for Computational Linguistics},
    location = {Dublin, Republic of Ireland},
    year = {2022},
    url = {https://arxiv.org/abs/2203.06482}
}
About Us

AUEB's Natural Language Processing Group develops algorithms, models, and systems that allow computers to process and generate natural language texts.

The group's current research interests include:

  • question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering,
  • natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content,
  • information extraction and opinion mining, including legal text analytics and sentiment analysis,
  • natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning.

The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business.

Manos Fergadiotis on behalf of AUEB's Natural Language Processing Group

Runs of nlpaueb sec-bert-base on huggingface.co

114.5K
Total runs
5.0K
24-hour runs
13.0K
3-day runs
29.8K
7-day runs
110.7K
30-day runs

More Information About sec-bert-base huggingface.co Model

More sec-bert-base license Visit here:

https://choosealicense.com/licenses/cc-by-sa-4.0

sec-bert-base huggingface.co

sec-bert-base huggingface.co is an AI model on huggingface.co that provides sec-bert-base's model effect (), which can be used instantly with this nlpaueb sec-bert-base model. huggingface.co supports a free trial of the sec-bert-base model, and also provides paid use of the sec-bert-base. Support call sec-bert-base model through api, including Node.js, Python, http.

sec-bert-base huggingface.co Url

https://huggingface.co/nlpaueb/sec-bert-base

nlpaueb sec-bert-base online free

sec-bert-base huggingface.co is an online trial and call api platform, which integrates sec-bert-base's modeling effects, including api services, and provides a free online trial of sec-bert-base, you can try sec-bert-base online for free by clicking the link below.

nlpaueb sec-bert-base online free url in huggingface.co:

https://huggingface.co/nlpaueb/sec-bert-base

sec-bert-base install

sec-bert-base is an open source model from GitHub that offers a free installation service, and any user can find sec-bert-base on GitHub to install. At the same time, huggingface.co provides the effect of sec-bert-base install, users can directly use sec-bert-base installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

sec-bert-base install url in huggingface.co:

https://huggingface.co/nlpaueb/sec-bert-base

Url of sec-bert-base

sec-bert-base huggingface.co Url

Provider of sec-bert-base huggingface.co

nlpaueb
ORGANIZATIONS
huggingface.co

Other API from nlpaueb

 huggingface.co
nlpaueb/sec-bert-num

Total runs: 244
Run Growth: 198
Growth Rate: 81.15%
Updated:April 28 2022