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update model card README.md
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README.md
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---
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license: mit
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: finetuned_minilm
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# finetuned_minilm
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This model is a fine-tuned version of [nreimers/MiniLM-L6-H384-uncased](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6736
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- Accuracy: 0.9023
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 12345
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- num_epochs: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|
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| 0.5371 | 1.0 | 619 | 0.2941 | 0.8782 |
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| 0.2763 | 2.0 | 1238 | 0.2590 | 0.8986 |
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| 0.1899 | 3.0 | 1857 | 0.3081 | 0.8959 |
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| 0.1257 | 4.0 | 2476 | 0.2576 | 0.9177 |
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| 0.0929 | 5.0 | 3095 | 0.3949 | 0.9059 |
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| 0.0806 | 6.0 | 3714 | 0.3304 | 0.9173 |
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| 0.0629 | 7.0 | 4333 | 0.4214 | 0.9073 |
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| 0.0474 | 8.0 | 4952 | 0.4625 | 0.9145 |
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| 0.0498 | 9.0 | 5571 | 0.4227 | 0.9236 |
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| 0.049 | 10.0 | 6190 | 0.5549 | 0.8945 |
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| 0.0411 | 11.0 | 6809 | 0.3340 | 0.9341 |
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| 0.0272 | 12.0 | 7428 | 0.3317 | 0.9291 |
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| 0.0264 | 13.0 | 8047 | 0.4099 | 0.9305 |
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| 0.0279 | 14.0 | 8666 | 0.4092 | 0.9268 |
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| 0.0242 | 15.0 | 9285 | 0.4418 | 0.9318 |
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| 0.0241 | 16.0 | 9904 | 0.4352 | 0.9273 |
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| 0.0238 | 17.0 | 10523 | 0.5306 | 0.9259 |
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| 0.0216 | 18.0 | 11142 | 0.4267 | 0.9241 |
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| 0.0166 | 19.0 | 11761 | 0.5134 | 0.9255 |
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| 0.0182 | 20.0 | 12380 | 0.6736 | 0.9023 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.10.1
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- Tokenizers 0.13.2
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