metadata
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-sentiment-model-roberta
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93
- name: F1
type: f1
value: 0.9297658862876254
- name: Precision
type: precision
value: 0.9328859060402684
- name: Recall
type: recall
value: 0.9266666666666666
finetuning-sentiment-model-roberta
This model was trained from scratch on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2171
- Accuracy: 0.93
- F1: 0.9298
- Precision: 0.9329
- Recall: 0.9267
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.144 | 0.98 | 46 | 0.2348 | 0.91 | 0.9132 | 0.8820 | 0.9467 |
| 0.0957 | 1.98 | 93 | 0.2171 | 0.93 | 0.9298 | 0.9329 | 0.9267 |
| 0.08 | 2.94 | 138 | 0.2554 | 0.9133 | 0.9167 | 0.8827 | 0.9533 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3