metadata
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.5931758530183727
- name: Recall
type: recall
value: 0.7371167645140247
- name: F1
type: f1
value: 0.6573589296102385
- name: Accuracy
type: accuracy
value: 0.896675559203776
model
This model is a fine-tuned version of roberta-large on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.5350
- Precision: 0.5932
- Recall: 0.7371
- F1: 0.6574
- Accuracy: 0.8967
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 0.47 | 466 | 0.5513 | 0.5389 | 0.7358 | 0.6222 | 0.8787 |
| 0.4041 | 1.47 | 932 | 0.5179 | 0.5398 | 0.7613 | 0.6317 | 0.8797 |
| 0.3968 | 2.07 | 1000 | 0.5350 | 0.5932 | 0.7371 | 0.6574 | 0.8967 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3