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--- |
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license: apache-2.0 |
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base_model: bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: BERT_without_preprocessing_grid_search |
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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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# BERT_without_preprocessing_grid_search |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6705 |
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- Precision: 0.8452 |
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- Recall: 0.8581 |
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- F1: 0.8510 |
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- Accuracy: 0.8818 |
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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: 3e-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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 257 | 0.5962 | 0.7414 | 0.8132 | 0.7626 | 0.8268 | |
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| 0.7597 | 2.0 | 514 | 0.5120 | 0.8170 | 0.8507 | 0.8292 | 0.8652 | |
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| 0.7597 | 3.0 | 771 | 0.4818 | 0.7975 | 0.8565 | 0.8202 | 0.8652 | |
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| 0.2391 | 4.0 | 1028 | 0.5223 | 0.8220 | 0.8613 | 0.8377 | 0.8652 | |
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| 0.2391 | 5.0 | 1285 | 0.5516 | 0.8172 | 0.8599 | 0.8347 | 0.8706 | |
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| 0.1316 | 6.0 | 1542 | 0.5747 | 0.8139 | 0.8593 | 0.8333 | 0.8710 | |
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| 0.1316 | 7.0 | 1799 | 0.6290 | 0.8332 | 0.8483 | 0.8386 | 0.8701 | |
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| 0.0773 | 8.0 | 2056 | 0.6089 | 0.8312 | 0.8620 | 0.8450 | 0.8764 | |
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| 0.0773 | 9.0 | 2313 | 0.6633 | 0.8384 | 0.8532 | 0.8448 | 0.8774 | |
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| 0.0633 | 10.0 | 2570 | 0.6705 | 0.8452 | 0.8581 | 0.8510 | 0.8818 | |
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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 2.14.4 |
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- Tokenizers 0.13.3 |
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