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MiniLMv2-L12-H384-emotion
philschmid/MiniLMv2-L12-H384-emotion
MiniLMv2-L12-H384-emotion is a text classification model from philschmid. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- roberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Dec 6, 2021
- SHA
- 4f973da9
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Datasets
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Mar 2, 2022
- Updated
- Dec 6, 2021