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finbert-tone-finetuned-finance-topic-classification

Text Classification
Transformers
PyTorch
TensorBoard
bert
generated_from_trainer
twitter-financial-topic-classification
financial
stocks
twitter
Eval Results
Inference Endpoints
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finbert-tone-finetuned-finance-topic-classification

This model is a fine-tuned version of yiyanghkust/finbert-tone on Twitter Financial News Topic dataset. It achieves the following results on the evaluation set:

  • Loss: 0.509021
  • Accuracy: 0.910615
  • F1: 0.910647
  • Precision: 0.911335
  • Recall: 0.910615

Model description

Model determines the financial topic of given tweets over 20 various topics. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance..

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 266 0.5152 0.8552 0.8504 0.8508 0.8552
0.7618 2.0 532 0.3999 0.8790 0.8781 0.8842 0.8790
0.7618 3.0 798 0.3628 0.8943 0.8940 0.8958 0.8943
0.16 4.0 1064 0.3776 0.8997 0.9001 0.9025 0.8997
0.16 5.0 1330 0.4286 0.8999 0.9002 0.9022 0.8999
0.058 6.0 1596 0.4500 0.9043 0.9042 0.9055 0.9043
0.058 7.0 1862 0.4689 0.9021 0.9017 0.9026 0.9021
0.0267 8.0 2128 0.4918 0.9031 0.9029 0.9039 0.9031
0.0267 9.0 2394 0.5030 0.9048 0.9049 0.9060 0.9048
0.0177 10.0 2660 0.5052 0.9033 0.9034 0.9044 0.9033
0.0177 11.0 2926 0.5265 0.9036 0.9034 0.9055 0.9036
0.013 12.0 3192 0.5267 0.9041 0.9041 0.9058 0.9041
0.013 13.0 3458 0.5090 0.9106 0.9106 0.9113 0.9106
0.0105 14.0 3724 0.5315 0.9067 0.9067 0.9080 0.9067
0.0105 15.0 3990 0.5339 0.9084 0.9084 0.9093 0.9084
0.0068 16.0 4256 0.5414 0.9072 0.9074 0.9088 0.9072
0.0051 17.0 4522 0.5460 0.9092 0.9091 0.9102 0.9092
0.0051 18.0 4788 0.5438 0.9072 0.9073 0.9081 0.9072
0.0035 19.0 5054 0.5474 0.9072 0.9073 0.9080 0.9072
0.0035 20.0 5320 0.5484 0.9079 0.9080 0.9087 0.9079

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2
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Evaluation results

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