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financial_bert

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metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: whataboutyou-ai/financial_bert
    results: []
language:
  - en
datasets:
  - expertai/BUSTER

financial_bert

This model is a fine-tuned version of bert-base-uncased on the BUSTER dataset. This model is ready to use for Named Entity Recognition (NER).

It achieves the following results on the evaluation set:

  • Loss: 0.0201
  • Precision: 0.7977
  • Recall: 0.8532
  • F1: 0.8245
  • Accuracy: 0.9937

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

This model was fine-tuned on the BUSTER dataset.

The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes:

Entity Description
O Outside of a named entity
B-Generic_Info.ANNUAL_REVENUES Beginning of annual revenues entity
I-Generic_Info.ANNUAL_REVENUES Continuation of annual revenues entity
B-Parties.ACQUIRED_COMPANY Beginning of acquired company entity
I-Parties.ACQUIRED_COMPANY Continuation of acquired company entity
B-Parties.BUYING_COMPANY Beginning of buying company entity
I-Parties.BUYING_COMPANY Continuation of buying company entity
B-Parties.SELLING_COMPANY Beginning of selling company entity
I-Parties.SELLING_COMPANY Continuation of selling company entity
B-Advisors.GENERIC_CONSULTING_COMPANY Beginning of generic consulting company entity
I-Advisors.GENERIC_CONSULTING_COMPANY Continuation of generic consulting company entity
B-Advisors.LEGAL_CONSULTING_COMPANY Beginning of legal consulting company entity
I-Advisors.LEGAL_CONSULTING_COMPANY Continuation of legal consulting company entity

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 3.0

Training results

Framework versions

  • Transformers 4.48.0.dev0
  • Pytorch 2.5.1
  • Datasets 3.1.0
  • Tokenizers 0.21.0