metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- f1
- accuracy
model-index:
- name: phrasebank-sentiment-analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
config: sentences_50agree
split: train
args: sentences_50agree
metrics:
- name: F1
type: f1
value: 0.8584242505968677
- name: Accuracy
type: accuracy
value: 0.8700137551581844
widget:
- text: >-
In the fourth quarter of 2009 , Orion 's net profit went up by 33.8 %
year-on-year to EUR33m .
phrasebank-sentiment-analysis
This model is a fine-tuned version of bert-base-uncased on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.4698
- F1: 0.8584
- Accuracy: 0.8700
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.6113 | 0.94 | 100 | 0.4105 | 0.8210 | 0.8487 |
| 0.2869 | 1.89 | 200 | 0.3898 | 0.8331 | 0.8618 |
| 0.1563 | 2.83 | 300 | 0.4733 | 0.8356 | 0.8425 |
| 0.073 | 3.77 | 400 | 0.4698 | 0.8584 | 0.8700 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1