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