metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes_movie_review
metrics:
- accuracy
- f1
- precision
- recall
base_model: distilbert-base-uncased
model-index:
- name: my_distilbert_model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: rotten_tomatoes_movie_review
type: rotten_tomatoes_movie_review
config: default
split: test
args: default
metrics:
- type: accuracy
value: 0.8433395872420263
name: Accuracy
- type: f1
value: 0.8433361406139583
name: F1
- type: precision
value: 0.8433698039878337
name: Precision
- type: recall
value: 0.8433395872420263
name: Recall
my_distilbert_model
This model is a fine-tuned version of distilbert-base-uncased on the rotten_tomatoes_movie_review dataset. It achieves the following results on the evaluation set:
- Loss: 0.4418
- Accuracy: 0.8433
- F1: 0.8433
- Precision: 0.8434
- Recall: 0.8433
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 267 | 0.3990 | 0.8246 | 0.8243 | 0.8269 | 0.8246 |
| 0.3534 | 2.0 | 534 | 0.3951 | 0.8452 | 0.8452 | 0.8452 | 0.8452 |
| 0.3534 | 3.0 | 801 | 0.4418 | 0.8433 | 0.8433 | 0.8434 | 0.8433 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3