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