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---
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
base_model: nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large
model-index:
- name: MiniLMv2-L12-H384-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- type: accuracy
value: 0.925
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MiniLMv2-L12-H384-emotion
This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2069
- Accuracy: 0.925
## 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: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8745 | 1.0 | 1000 | 0.6673 | 0.81 |
| 0.3466 | 2.0 | 2000 | 0.2816 | 0.918 |
| 0.2201 | 3.0 | 3000 | 0.2367 | 0.9215 |
| 0.1761 | 4.0 | 4000 | 0.2069 | 0.925 |
| 0.1435 | 5.0 | 5000 | 0.2089 | 0.922 |
| 0.1454 | 6.0 | 6000 | 0.2168 | 0.923 |
| 0.1041 | 7.0 | 7000 | 0.2081 | 0.924 |
| 0.0953 | 8.0 | 8000 | 0.2133 | 0.9245 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|