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: 0.8419033782047481
- name: Accuracy
type: accuracy
value: 0.8541953232462174
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: 0.5105
- F1: 0.8419
- Accuracy: 0.8542
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 |
|---|---|---|---|---|---|
| 0.6046 | 0.94 | 100 | 0.4107 | 0.8173 | 0.8370 |
| 0.2873 | 1.89 | 200 | 0.4488 | 0.8266 | 0.8301 |
| 0.1469 | 2.83 | 300 | 0.5130 | 0.8420 | 0.8501 |
| 0.0762 | 3.77 | 400 | 0.5105 | 0.8419 | 0.8542 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1