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Anwaarma
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I04-PC

Text Classification
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bert
Generated from Trainer
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  1. README.md +86 -0
  2. runs/Dec24_16-13-48_86f59ffbd25a/events.out.tfevents.1703434429.86f59ffbd25a.1423.56 +2 -2
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  5. runs/Dec24_16-13-48_86f59ffbd25a/events.out.tfevents.1703434454.86f59ffbd25a.1423.59 +3 -0
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: prajjwal1/bert-tiny
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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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+ - f1
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+ model-index:
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+ - name: I04-PC
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+ results: []
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+ ---
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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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+
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+ # I04-PC
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+
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+ This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0916
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+ - Accuracy: 0.98
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+ - F1: 0.9899
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 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: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 0.01 | 50 | 0.6723 | 0.66 | 0.6376 |
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+ | No log | 0.02 | 100 | 0.5802 | 0.71 | 0.7065 |
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+ | No log | 0.03 | 150 | 0.4534 | 0.87 | 0.8701 |
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+ | No log | 0.04 | 200 | 0.3370 | 0.88 | 0.88 |
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+ | No log | 0.05 | 250 | 0.2473 | 0.94 | 0.9394 |
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+ | No log | 0.06 | 300 | 0.2231 | 0.93 | 0.9295 |
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+ | No log | 0.07 | 350 | 0.1813 | 0.94 | 0.9394 |
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+ | No log | 0.08 | 400 | 0.1519 | 0.96 | 0.9599 |
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+ | No log | 0.09 | 450 | 0.1526 | 0.96 | 0.9599 |
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+ | 0.3852 | 0.1 | 500 | 0.1554 | 0.96 | 0.9599 |
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+ | 0.3852 | 0.11 | 550 | 0.1495 | 0.96 | 0.9599 |
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+ | 0.3852 | 0.12 | 600 | 0.1206 | 0.96 | 0.9599 |
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+ | 0.3852 | 0.13 | 650 | 0.1013 | 0.96 | 0.9599 |
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+ | 0.3852 | 0.14 | 700 | 0.0592 | 0.99 | 0.9900 |
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+ | 0.3852 | 0.15 | 750 | 0.0583 | 0.99 | 0.9900 |
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+ | 0.3852 | 0.16 | 800 | 0.0551 | 0.99 | 0.9900 |
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+ | 0.3852 | 0.17 | 850 | 0.0540 | 0.99 | 0.9900 |
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+ | 0.3852 | 0.18 | 900 | 0.0539 | 0.99 | 0.9900 |
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+ | 0.3852 | 0.19 | 950 | 0.0537 | 0.99 | 0.9900 |
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+ | 0.1428 | 0.2 | 1000 | 0.0533 | 0.99 | 0.9900 |
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+ | 0.1428 | 0.2 | 1050 | 0.0528 | 0.99 | 0.9900 |
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+ | 0.1428 | 0.21 | 1100 | 0.0510 | 0.99 | 0.9900 |
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+ | 0.1428 | 0.22 | 1150 | 0.0524 | 0.99 | 0.9900 |
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+ | 0.1428 | 0.23 | 1200 | 0.0524 | 0.99 | 0.9900 |
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+ | 0.1428 | 0.24 | 1250 | 0.0510 | 0.99 | 0.9900 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.0
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+ - Tokenizers 0.15.0
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