---
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](https://huggingface.co/bert-base-uncased) on the [BUSTER](https://huggingface.co/datasets/expertai/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](https://huggingface.co/datasets/expertai/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