metadata
library_name: transformers
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: HarmCare_binary
results: []
HarmCare_binary
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6675
- Accuracy: 0.6433
- Precision: 0.6315
- Recall: 0.7438
- F1: 0.6831
- Auc: 0.6398
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 | Precision | Recall | F1 | Auc |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 127 | 0.6639 | 0.6087 | 0.6074 | 0.6864 | 0.6445 | 0.6060 |
| No log | 2.0 | 254 | 0.6713 | 0.5988 | 0.5723 | 0.8853 | 0.6952 | 0.5889 |
| No log | 3.0 | 381 | 0.6675 | 0.6433 | 0.6315 | 0.7438 | 0.6831 | 0.6398 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1