metadata
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.94
MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc
This model is a fine-tuned version of nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.3479
- Accuracy: 0.94
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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 60 | 0.8171 | 0.2490 |
| No log | 2.0 | 120 | 0.7039 | 0.6568 |
| No log | 3.0 | 180 | 0.6067 | 0.7932 |
| 0.7269 | 4.0 | 240 | 0.5270 | 0.8674 |
| 0.7269 | 5.0 | 300 | 0.4659 | 0.9010 |
| 0.7269 | 6.0 | 360 | 0.4201 | 0.9194 |
| 0.7269 | 7.0 | 420 | 0.3867 | 0.9352 |
| 0.4426 | 8.0 | 480 | 0.3649 | 0.9352 |
| 0.4426 | 9.0 | 540 | 0.3520 | 0.9403 |
| 0.4426 | 10.0 | 600 | 0.3479 | 0.94 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3