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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/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_ST_DA_100_v2 |
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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_ST_DA_100_v2 |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/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: 0.2371 |
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- Precision: 0.9457 |
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- Recall: 0.9480 |
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- F1: 0.9469 |
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- Accuracy: 0.9446 |
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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: 16 |
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- eval_batch_size: 16 |
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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 | 59 | 0.3489 | 0.9065 | 0.9194 | 0.9129 | 0.9085 | |
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| No log | 2.0 | 118 | 0.2883 | 0.9190 | 0.9267 | 0.9228 | 0.9180 | |
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| No log | 3.0 | 177 | 0.2505 | 0.9322 | 0.9403 | 0.9362 | 0.9330 | |
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| No log | 4.0 | 236 | 0.2300 | 0.9384 | 0.9446 | 0.9415 | 0.9384 | |
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| No log | 5.0 | 295 | 0.2305 | 0.9397 | 0.9435 | 0.9416 | 0.9386 | |
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| No log | 6.0 | 354 | 0.2332 | 0.9443 | 0.9482 | 0.9462 | 0.9438 | |
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| No log | 7.0 | 413 | 0.2341 | 0.9433 | 0.9468 | 0.9450 | 0.9429 | |
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| No log | 8.0 | 472 | 0.2364 | 0.9441 | 0.9474 | 0.9457 | 0.9430 | |
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| 0.1814 | 9.0 | 531 | 0.2339 | 0.9457 | 0.9472 | 0.9465 | 0.9439 | |
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| 0.1814 | 10.0 | 590 | 0.2371 | 0.9457 | 0.9480 | 0.9469 | 0.9446 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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