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--- |
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license: mit |
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base_model: microsoft/MiniLM-L12-H384-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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- precision |
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- recall |
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model-index: |
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- name: 019-microsoft-MiniLM-finetuned-yahoo-80000_20000 |
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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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# 019-microsoft-MiniLM-finetuned-yahoo-80000_20000 |
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This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co./microsoft/MiniLM-L12-H384-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8508 |
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- F1: 0.7322 |
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- Accuracy: 0.7357 |
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- Precision: 0.7318 |
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- Recall: 0.7357 |
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- System Ram Used: 4.0900 |
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- System Ram Total: 83.4807 |
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- Gpu Ram Allocated: 0.3934 |
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- Gpu Ram Cached: 16.0508 |
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- Gpu Ram Total: 39.5640 |
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- Gpu Utilization: 31 |
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- Disk Space Used: 26.4706 |
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- Disk Space Total: 78.1898 |
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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: 2e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | System Ram Used | System Ram Total | Gpu Ram Allocated | Gpu Ram Cached | Gpu Ram Total | Gpu Utilization | Disk Space Used | Disk Space Total | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:|:---------:|:------:|:---------------:|:----------------:|:-----------------:|:--------------:|:-------------:|:---------------:|:---------------:|:----------------:| |
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| 1.5316 | 0.25 | 625 | 1.1302 | 0.6824 | 0.6928 | 0.6859 | 0.6928 | 4.1089 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 33 | 25.7180 | 78.1898 | |
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| 1.0615 | 0.5 | 1250 | 1.0022 | 0.7011 | 0.7049 | 0.7065 | 0.7049 | 3.8585 | 83.4807 | 0.3936 | 16.0508 | 39.5640 | 33 | 26.0913 | 78.1898 | |
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| 0.9804 | 0.75 | 1875 | 0.9258 | 0.7158 | 0.7191 | 0.7201 | 0.7191 | 3.8640 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 33 | 26.4646 | 78.1898 | |
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| 0.9244 | 1.0 | 2500 | 0.8795 | 0.7219 | 0.7286 | 0.7266 | 0.7286 | 3.8815 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 32 | 26.4649 | 78.1898 | |
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| 0.8471 | 1.25 | 3125 | 0.8886 | 0.7243 | 0.7305 | 0.7280 | 0.7305 | 4.0318 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 31 | 26.4653 | 78.1898 | |
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| 0.8294 | 1.5 | 3750 | 0.8648 | 0.7285 | 0.7303 | 0.7304 | 0.7303 | 3.8228 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 33 | 26.4656 | 78.1898 | |
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| 0.8229 | 1.75 | 4375 | 0.8477 | 0.7306 | 0.7347 | 0.7314 | 0.7347 | 3.8704 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 32 | 26.4658 | 78.1898 | |
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| 0.8227 | 2.0 | 5000 | 0.8514 | 0.7300 | 0.7321 | 0.7343 | 0.7321 | 3.8656 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 34 | 26.4661 | 78.1898 | |
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| 0.7515 | 2.25 | 5625 | 0.8580 | 0.7286 | 0.7327 | 0.7324 | 0.7327 | 4.0576 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 32 | 26.4664 | 78.1898 | |
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| 0.7523 | 2.5 | 6250 | 0.8498 | 0.7296 | 0.734 | 0.7314 | 0.734 | 3.8656 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 32 | 26.4666 | 78.1898 | |
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| 0.7396 | 2.75 | 6875 | 0.8403 | 0.7326 | 0.7365 | 0.7323 | 0.7365 | 3.8686 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 33 | 26.4669 | 78.1898 | |
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| 0.7308 | 3.0 | 7500 | 0.8414 | 0.7348 | 0.7378 | 0.7339 | 0.7378 | 3.8611 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 26 | 26.4671 | 78.1898 | |
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| 0.6929 | 3.25 | 8125 | 0.8551 | 0.7322 | 0.7350 | 0.7376 | 0.7350 | 4.0565 | 83.4807 | 0.3936 | 16.0508 | 39.5640 | 29 | 26.4680 | 78.1898 | |
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| 0.6772 | 3.5 | 8750 | 0.8471 | 0.7335 | 0.738 | 0.7327 | 0.738 | 3.8351 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 31 | 26.4684 | 78.1898 | |
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| 0.682 | 3.75 | 9375 | 0.8460 | 0.7311 | 0.735 | 0.7311 | 0.735 | 3.8782 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 34 | 26.4686 | 78.1898 | |
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| 0.6741 | 4.0 | 10000 | 0.8409 | 0.7335 | 0.7376 | 0.7330 | 0.7376 | 3.8848 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 31 | 26.4690 | 78.1898 | |
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| 0.6247 | 4.25 | 10625 | 0.8500 | 0.7332 | 0.736 | 0.7324 | 0.736 | 4.0838 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 32 | 26.4694 | 78.1898 | |
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| 0.6446 | 4.5 | 11250 | 0.8464 | 0.7323 | 0.7358 | 0.7320 | 0.7358 | 3.8687 | 83.4807 | 0.3936 | 16.0508 | 39.5640 | 31 | 26.4697 | 78.1898 | |
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| 0.6355 | 4.75 | 11875 | 0.8503 | 0.7311 | 0.7349 | 0.7308 | 0.7349 | 3.8853 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 30 | 26.4700 | 78.1898 | |
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| 0.6396 | 5.0 | 12500 | 0.8508 | 0.7322 | 0.7357 | 0.7318 | 0.7357 | 3.8995 | 83.4807 | 0.3935 | 16.0508 | 39.5640 | 33 | 26.4704 | 78.1898 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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