metadata
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- accuracy
base_model: SALT-NLP/FLANG-BERT
model-index:
- name: sentiment_bert
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
config: sentences_66agree
split: train
args: sentences_66agree
metrics:
- type: accuracy
value: 0.9360189573459715
name: Accuracy
sentiment_bert
This model is a fine-tuned version of SALT-NLP/FLANG-BERT on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.3754
- Accuracy: 0.9360
Model description
More information needed
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2