metadata
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
datasets:
- RobZamp/sick
metrics:
- accuracy
model-index:
- name: roberta-large-fp-sick
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sick
type: RobZamp/sick
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.898989898989899
roberta-large-fp-sick
This model is a fine-tuned version of roberta-large on the sick dataset. It achieves the following results on the evaluation set:
- Loss: 0.2761
- Accuracy: 0.8990
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: 64
- eval_batch_size: 32
- seed: 38
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 70 | 0.3558 | 0.8465 |
| No log | 2.0 | 140 | 0.3003 | 0.8949 |
| No log | 3.0 | 210 | 0.2761 | 0.8990 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0