metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples-6pm
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Precision
type: precision
value: 0.875
- name: Recall
type: recall
value: 0.8866666666666667
- name: F1
type: f1
value: 0.880794701986755
- name: Accuracy
type: accuracy
value: 0.88
finetuning-sentiment-model-3000-samples-6pm
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2896
- Precision: 0.875
- Recall: 0.8867
- F1: 0.8808
- Accuracy: 0.88
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: 1e-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: 11
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 188 | 0.3436 | 0.8633 | 0.8 | 0.8304 | 0.8367 |
| No log | 2.0 | 376 | 0.2896 | 0.875 | 0.8867 | 0.8808 | 0.88 |
| 0.3 | 3.0 | 564 | 0.3330 | 0.8693 | 0.8867 | 0.8779 | 0.8767 |
| 0.3 | 4.0 | 752 | 0.4378 | 0.8766 | 0.9 | 0.8882 | 0.8867 |
| 0.3 | 5.0 | 940 | 0.5198 | 0.8284 | 0.9333 | 0.8777 | 0.87 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1