metadata
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
base_model: nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large
model-index:
- name: MiniLMv2-L6-H384-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: accuracy
value: 0.9215
name: Accuracy
MiniLMv2-L6-H384-emotion
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.2140
- Accuracy: 0.9215
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: 3e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.432 | 1.0 | 500 | 0.9992 | 0.6805 |
| 0.8073 | 2.0 | 1000 | 0.5437 | 0.846 |
| 0.4483 | 3.0 | 1500 | 0.3018 | 0.909 |
| 0.2833 | 4.0 | 2000 | 0.2412 | 0.915 |
| 0.2169 | 5.0 | 2500 | 0.2140 | 0.9215 |
| 0.1821 | 6.0 | 3000 | 0.2159 | 0.917 |
| 0.154 | 7.0 | 3500 | 0.2084 | 0.919 |
| 0.1461 | 8.0 | 4000 | 0.2047 | 0.92 |
Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3