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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- sibyl |
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datasets: |
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- yelp_polarity |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert-base-uncased-yelp_polarity |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: yelp_polarity |
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type: yelp_polarity |
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args: plain_text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9516052631578947 |
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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-base-uncased-yelp_polarity |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the yelp_polarity dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3222 |
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- Accuracy: 0.9516 |
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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: 1 |
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- eval_batch_size: 8 |
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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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- lr_scheduler_warmup_steps: 277200 |
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- training_steps: 2772000 |
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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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| 0.8067 | 0.0 | 2000 | 0.8241 | 0.4975 | |
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| 0.5482 | 0.01 | 4000 | 0.3507 | 0.8591 | |
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| 0.3427 | 0.01 | 6000 | 0.3750 | 0.9139 | |
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| 0.4133 | 0.01 | 8000 | 0.5520 | 0.9016 | |
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| 0.4301 | 0.02 | 10000 | 0.3803 | 0.9304 | |
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| 0.3716 | 0.02 | 12000 | 0.4168 | 0.9337 | |
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| 0.4076 | 0.03 | 14000 | 0.5042 | 0.9170 | |
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| 0.3674 | 0.03 | 16000 | 0.4806 | 0.9268 | |
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| 0.3813 | 0.03 | 18000 | 0.4227 | 0.9261 | |
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| 0.3723 | 0.04 | 20000 | 0.3360 | 0.9418 | |
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| 0.3876 | 0.04 | 22000 | 0.3255 | 0.9407 | |
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| 0.3351 | 0.04 | 24000 | 0.3283 | 0.9404 | |
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| 0.34 | 0.05 | 26000 | 0.3489 | 0.9430 | |
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| 0.3006 | 0.05 | 28000 | 0.3302 | 0.9464 | |
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| 0.349 | 0.05 | 30000 | 0.3853 | 0.9375 | |
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| 0.3696 | 0.06 | 32000 | 0.2992 | 0.9454 | |
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| 0.3301 | 0.06 | 34000 | 0.3484 | 0.9464 | |
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| 0.3151 | 0.06 | 36000 | 0.3529 | 0.9455 | |
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| 0.3682 | 0.07 | 38000 | 0.3052 | 0.9420 | |
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| 0.3184 | 0.07 | 40000 | 0.3323 | 0.9466 | |
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| 0.3207 | 0.08 | 42000 | 0.3133 | 0.9532 | |
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| 0.3346 | 0.08 | 44000 | 0.3826 | 0.9414 | |
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| 0.3008 | 0.08 | 46000 | 0.3059 | 0.9484 | |
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| 0.3306 | 0.09 | 48000 | 0.3089 | 0.9475 | |
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| 0.342 | 0.09 | 50000 | 0.3611 | 0.9486 | |
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| 0.3424 | 0.09 | 52000 | 0.3227 | 0.9445 | |
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| 0.3044 | 0.1 | 54000 | 0.3130 | 0.9489 | |
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| 0.3278 | 0.1 | 56000 | 0.3827 | 0.9368 | |
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| 0.288 | 0.1 | 58000 | 0.3080 | 0.9504 | |
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| 0.3342 | 0.11 | 60000 | 0.3252 | 0.9471 | |
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| 0.3737 | 0.11 | 62000 | 0.4250 | 0.9343 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.7.1 |
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- Datasets 1.6.1 |
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- Tokenizers 0.10.3 |
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