metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- movie_releases
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-radarr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: movie_releases
type: movie_releases
args: default
metrics:
- name: Precision
type: precision
value: 0.9555421444377389
- name: Recall
type: recall
value: 0.9638798701298701
- name: F1
type: f1
value: 0.9596928982725529
- name: Accuracy
type: accuracy
value: 0.9817602584524263
bert-finetuned-radarr
This model is a fine-tuned version of distilbert-base-uncased on the movie_releases dataset. It achieves the following results on the evaluation set:
- Loss: 0.0731
- Precision: 0.9555
- Recall: 0.9639
- F1: 0.9597
- Accuracy: 0.9818
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0431 | 1.0 | 1191 | 0.1403 | 0.9436 | 0.9574 | 0.9504 | 0.9626 |
| 0.0236 | 2.0 | 2382 | 0.0881 | 0.9485 | 0.9560 | 0.9522 | 0.9694 |
| 0.0138 | 3.0 | 3573 | 0.0731 | 0.9555 | 0.9639 | 0.9597 | 0.9818 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1