metadata
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-roberta-base-25000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9476
- name: F1
type: f1
value: 0.9488481062085123
finetuning-sentiment-model-roberta-base-25000-samples
This model is a fine-tuned version of roberta-base on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.3321
- Accuracy: 0.9476
- F1: 0.9488
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2475 | 1.0 | 1407 | 0.2287 | 0.936 | 0.9383 |
| 0.1528 | 2.0 | 2814 | 0.2354 | 0.9328 | 0.9319 |
| 0.0888 | 3.0 | 4221 | 0.2754 | 0.9432 | 0.9452 |
| 0.0476 | 4.0 | 5628 | 0.2962 | 0.9464 | 0.9475 |
| 0.0275 | 5.0 | 7035 | 0.3321 | 0.9476 | 0.9488 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1