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End of training

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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: Merged-MM-praj
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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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+ # Merged-MM-praj
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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 the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5525
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+ - Accuracy: 0.7777
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+ - F1: 0.8749
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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: 3e-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: 7
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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 | 0.0 | 50 | 0.6929 | 0.526 | 0.3813 |
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+ | No log | 0.0 | 100 | 0.6938 | 0.48 | 0.3125 |
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+ | No log | 0.01 | 150 | 0.6971 | 0.479 | 0.3103 |
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+ | No log | 0.01 | 200 | 0.6948 | 0.479 | 0.3103 |
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+ | No log | 0.01 | 250 | 0.6938 | 0.479 | 0.3103 |
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+ | No log | 0.01 | 300 | 0.6939 | 0.479 | 0.3103 |
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+ | No log | 0.01 | 350 | 0.6927 | 0.521 | 0.3587 |
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+ | No log | 0.02 | 400 | 0.6931 | 0.501 | 0.4988 |
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+ | No log | 0.02 | 450 | 0.6944 | 0.479 | 0.3103 |
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+ | 0.6942 | 0.02 | 500 | 0.6954 | 0.479 | 0.3103 |
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+ | 0.6942 | 0.02 | 550 | 0.6960 | 0.479 | 0.3103 |
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+ | 0.6942 | 0.02 | 600 | 0.6934 | 0.486 | 0.3322 |
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+ | 0.6942 | 0.02 | 650 | 0.6970 | 0.479 | 0.3103 |
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+ | 0.6942 | 0.03 | 700 | 0.6929 | 0.535 | 0.4767 |
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+ | 0.6942 | 0.03 | 750 | 0.6931 | 0.499 | 0.4609 |
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+ | 0.6942 | 0.03 | 800 | 0.6952 | 0.479 | 0.3103 |
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+ | 0.6942 | 0.03 | 850 | 0.6933 | 0.48 | 0.3160 |
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+ | 0.6942 | 0.03 | 900 | 0.6979 | 0.479 | 0.3103 |
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+ | 0.6942 | 0.04 | 950 | 0.6940 | 0.479 | 0.3103 |
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+ | 0.6938 | 0.04 | 1000 | 0.6915 | 0.521 | 0.3569 |
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+ | 0.6938 | 0.04 | 1050 | 0.6942 | 0.479 | 0.3103 |
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+ | 0.6938 | 0.04 | 1100 | 0.6884 | 0.519 | 0.3630 |
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+ | 0.6938 | 0.04 | 1150 | 0.6849 | 0.596 | 0.5817 |
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+ | 0.6938 | 0.05 | 1200 | 0.6849 | 0.547 | 0.5131 |
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+ | 0.6938 | 0.05 | 1250 | 0.6771 | 0.568 | 0.5502 |
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+ | 0.6938 | 0.05 | 1300 | 0.6792 | 0.572 | 0.5558 |
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+ | 0.6938 | 0.05 | 1350 | 0.6889 | 0.55 | 0.5161 |
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+ | 0.6938 | 0.05 | 1400 | 0.6792 | 0.59 | 0.5828 |
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+ | 0.6938 | 0.06 | 1450 | 0.6729 | 0.602 | 0.5987 |
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+ | 0.6781 | 0.06 | 1500 | 0.6702 | 0.592 | 0.5822 |
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+ | 0.6781 | 0.06 | 1550 | 0.6711 | 0.578 | 0.5633 |
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+ | 0.6781 | 0.06 | 1600 | 0.6642 | 0.607 | 0.6024 |
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+ | 0.6781 | 0.06 | 1650 | 0.6624 | 0.592 | 0.5819 |
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+ | 0.6781 | 0.07 | 1700 | 0.6585 | 0.595 | 0.5883 |
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+ | 0.6781 | 0.07 | 1750 | 0.6543 | 0.584 | 0.5740 |
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+ | 0.6781 | 0.07 | 1800 | 0.6452 | 0.6 | 0.5926 |
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+ | 0.6781 | 0.07 | 1850 | 0.6355 | 0.615 | 0.6106 |
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+ | 0.6781 | 0.07 | 1900 | 0.6280 | 0.615 | 0.6090 |
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+ | 0.6781 | 0.07 | 1950 | 0.6209 | 0.621 | 0.6139 |
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+ | 0.6465 | 0.08 | 2000 | 0.6178 | 0.632 | 0.6247 |
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+ | 0.6465 | 0.08 | 2050 | 0.6133 | 0.641 | 0.6303 |
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+ | 0.6465 | 0.08 | 2100 | 0.6132 | 0.629 | 0.6218 |
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+ | 0.6465 | 0.08 | 2150 | 0.6155 | 0.63 | 0.6289 |
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+ | 0.6465 | 0.08 | 2200 | 0.5984 | 0.635 | 0.6322 |
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+ | 0.6465 | 0.09 | 2250 | 0.6065 | 0.633 | 0.6102 |
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+ | 0.6465 | 0.09 | 2300 | 0.5968 | 0.629 | 0.6063 |
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+ | 0.6465 | 0.09 | 2350 | 0.5871 | 0.649 | 0.6411 |
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+ | 0.6465 | 0.09 | 2400 | 0.5824 | 0.64 | 0.6218 |
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+ | 0.6465 | 0.09 | 2450 | 0.5812 | 0.643 | 0.6390 |
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+ | 0.6042 | 0.1 | 2500 | 0.5790 | 0.644 | 0.6355 |
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+ | 0.6042 | 0.1 | 2550 | 0.5744 | 0.654 | 0.6507 |
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+ | 0.6042 | 0.1 | 2600 | 0.5679 | 0.641 | 0.6292 |
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+ | 0.6042 | 0.1 | 2650 | 0.5707 | 0.644 | 0.6311 |
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+ | 0.6042 | 0.1 | 2700 | 0.5707 | 0.652 | 0.6439 |
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+ | 0.6042 | 0.11 | 2750 | 0.5680 | 0.661 | 0.6569 |
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+ | 0.6042 | 0.11 | 2800 | 0.5592 | 0.67 | 0.6684 |
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+ | 0.6042 | 0.11 | 2850 | 0.5557 | 0.678 | 0.6758 |
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+ | 0.6042 | 0.11 | 2900 | 0.5579 | 0.671 | 0.6690 |
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+ | 0.6042 | 0.11 | 2950 | 0.5490 | 0.692 | 0.6909 |
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+ | 0.5834 | 0.11 | 3000 | 0.5474 | 0.688 | 0.6858 |
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+ | 0.5834 | 0.12 | 3050 | 0.5447 | 0.696 | 0.6902 |
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+ | 0.5834 | 0.12 | 3100 | 0.5456 | 0.699 | 0.6985 |
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+ | 0.5834 | 0.12 | 3150 | 0.5592 | 0.675 | 0.6628 |
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+ | 0.5834 | 0.12 | 3200 | 0.5442 | 0.69 | 0.6856 |
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+ | 0.5834 | 0.12 | 3250 | 0.5424 | 0.698 | 0.6974 |
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+ | 0.5834 | 0.13 | 3300 | 0.5464 | 0.691 | 0.6907 |
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+ | 0.5834 | 0.13 | 3450 | 0.5406 | 0.712 | 0.7091 |
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+ | 0.5551 | 0.13 | 3500 | 0.5367 | 0.738 | 0.7376 |
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+ | 0.5551 | 0.14 | 3550 | 0.5354 | 0.713 | 0.7091 |
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+ | 0.5551 | 0.14 | 3600 | 0.5377 | 0.74 | 0.7400 |
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+ | 0.5551 | 0.14 | 3650 | 0.5342 | 0.751 | 0.7506 |
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+ | 0.5551 | 0.14 | 3750 | 0.5395 | 0.737 | 0.7368 |
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+ | 0.5551 | 0.15 | 3850 | 0.5245 | 0.737 | 0.7371 |
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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
config.json CHANGED
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  "hidden_act": "gelu",
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 128,
 
 
 
 
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  "initializer_range": 0.02,
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  "intermediate_size": 512,
 
 
 
 
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  "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
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  "model_type": "bert",
 
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  "hidden_act": "gelu",
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 128,
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+ "1": "Not Taken"
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+ },
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  "initializer_range": 0.02,
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  "intermediate_size": 512,
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+ "label2id": {
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+ "Not Taken": 1,
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+ "Taken": 0
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+ },
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  "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
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  "model_type": "bert",
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