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---
license: mit
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: minilm-finetuned-emotion_nm
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: F1
type: f1
value: 0.9322805793931607
---
<!-- 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. -->
# minilm-finetuned-emotion_nm
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1918
- F1: 0.9323
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.3627 | 1.0 | 250 | 1.0048 | 0.5936 |
| 0.8406 | 2.0 | 500 | 0.6477 | 0.8608 |
| 0.5344 | 3.0 | 750 | 0.4025 | 0.9099 |
| 0.3619 | 4.0 | 1000 | 0.3142 | 0.9188 |
| 0.274 | 5.0 | 1250 | 0.2489 | 0.9277 |
| 0.2225 | 6.0 | 1500 | 0.2320 | 0.9303 |
| 0.191 | 7.0 | 1750 | 0.2083 | 0.9298 |
| 0.1731 | 8.0 | 2000 | 0.1969 | 0.9334 |
| 0.1606 | 9.0 | 2250 | 0.1928 | 0.9362 |
| 0.1462 | 10.0 | 2500 | 0.1918 | 0.9323 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
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