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MiniLMv2-L6-H768-sst2
philschmid/MiniLMv2-L6-H768-sst2
MiniLMv2-L6-H768-sst2 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
- Apr 8, 2022
- Updated
- Apr 8, 2022
- SHA
- cb11bf29
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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-L6-H768-sst2
This model is a fine-tuned version of nreimers/MiniLMv2-L6-H768-distilled-from-RoBERTa-Large on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.2013
- Accuracy: 0.9427
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
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4734 | 1.0 | 264 | 0.2046 | 0.9243 |
| 0.2399 | 2.0 | 528 | 0.1912 | 0.9346 |
| 0.1791 | 3.0 | 792 | 0.1943 | 0.9335 |
| 0.1442 | 4.0 | 1056 | 0.2103 | 0.9369 |
| 0.1217 | 5.0 | 1320 | 0.2013 | 0.9427 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- Created
- Apr 8, 2022
- Updated
- Apr 8, 2022