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---
license: mit
base_model: microsoft/MiniLM-L12-H384-uncased
tags:
- Language
- image-Emotion
- miniLM
- PyTorch
- Trainer
- SequenceClassification
- WeightedLoss
- CrossEntropyLoss
- F1Score
- HuggingFaceHub
- generated_from_trainer
datasets:
- emotion
metrics:
- f1
model-index:
- name: miniLM_finetuned_Emotion_2024_06_15
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: F1
type: f1
value: 0.927424135409491
---
<!-- 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_2024_06_15
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: 0.1881
- F1: 0.9274
## 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: 5e-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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1887 | 1.0 | 250 | 0.7672 | 0.7461 |
| 0.5641 | 2.0 | 500 | 0.3698 | 0.9058 |
| 0.3151 | 3.0 | 750 | 0.2783 | 0.9244 |
| 0.2074 | 4.0 | 1000 | 0.2417 | 0.9273 |
| 0.1586 | 5.0 | 1250 | 0.1749 | 0.9301 |
| 0.1287 | 6.0 | 1500 | 0.1945 | 0.9344 |
| 0.1112 | 7.0 | 1750 | 0.2054 | 0.9313 |
| 0.1031 | 8.0 | 2000 | 0.1677 | 0.9308 |
| 0.0819 | 9.0 | 2250 | 0.1862 | 0.9279 |
| 0.0743 | 10.0 | 2500 | 0.1881 | 0.9274 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|