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google_bert/bert_base_uncased_amazon/README.md ADDED
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+ ---
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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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+ - accuracy
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+ model-index:
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+ - name: bert_base_uncased_amazon
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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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+ # bert_base_uncased_amazon
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+
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7916
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+ - Accuracy: 0.7879
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+ - F1 Macro: 0.7308
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+ - F1 Micro: 0.7879
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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: 2e-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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+ - distributed_type: multi-GPU
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+ - num_devices: 2
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+ - total_train_batch_size: 64
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+ - total_eval_batch_size: 64
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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: 3.0
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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 Macro | F1 Micro |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
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+ | 2.5476 | 0.26 | 50 | 2.4071 | 0.4967 | 0.3546 | 0.4967 |
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+ | 1.7401 | 0.53 | 100 | 1.6470 | 0.6337 | 0.4899 | 0.6337 |
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+ | 1.3223 | 0.79 | 150 | 1.2889 | 0.6897 | 0.5665 | 0.6897 |
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+ | 1.1317 | 1.05 | 200 | 1.1047 | 0.7358 | 0.6577 | 0.7358 |
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+ | 0.9137 | 1.32 | 250 | 0.9907 | 0.7536 | 0.6820 | 0.7536 |
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+ | 0.9434 | 1.58 | 300 | 0.9264 | 0.7602 | 0.6896 | 0.7602 |
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+ | 0.86 | 1.84 | 350 | 0.8729 | 0.7780 | 0.7135 | 0.7780 |
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+ | 0.7567 | 2.11 | 400 | 0.8322 | 0.7859 | 0.7244 | 0.7859 |
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+ | 0.7028 | 2.37 | 450 | 0.8130 | 0.7892 | 0.7339 | 0.7892 |
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+ | 0.6842 | 2.63 | 500 | 0.8005 | 0.7892 | 0.7284 | 0.7892 |
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+ | 0.6784 | 2.89 | 550 | 0.7916 | 0.7879 | 0.7308 | 0.7879 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.0.dev0
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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+ "test_accuracy": 0.8201581027667985,
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+ "test_loss": 0.7230327725410461,
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+ "test_runtime": 1.4903,
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+ "test_samples_per_second": 1018.573,
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+ "test_steps_per_second": 16.104,
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+ "train_loss": 1.1961698414986595,
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+ "train_samples": 12144,
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+ "train_samples_per_second": 268.42,
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+ "train_steps_per_second": 4.2
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+ }
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+ ---
2
+ license: apache-2.0
3
+ base_model: google-bert/bert-base-uncased
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+ tags:
5
+ - generated_from_trainer
6
+ metrics:
7
+ - accuracy
8
+ model-index:
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+ - name: bert_base_uncased_ledgar
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+ results: []
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+ ---
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+
13
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
+ should probably proofread and complete it, then remove this comment. -->
15
+
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+ # bert_base_uncased_ledgar
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+
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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 an unknown dataset.
19
+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6676
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+ - Accuracy: 0.8349
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+ - F1 Macro: 0.7127
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+ - F1 Micro: 0.8349
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+
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+ ## Model description
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+
27
+ More information needed
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+
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+ ## Intended uses & limitations
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+
31
+ More information needed
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+
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+ ## Training and evaluation data
34
+
35
+ 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:
42
+ - learning_rate: 2e-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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+ - distributed_type: multi-GPU
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+ - num_devices: 2
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+ - total_train_batch_size: 64
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+ - total_eval_batch_size: 64
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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: 3.0
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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 Macro | F1 Micro |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|
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+ | 3.6919 | 0.11 | 100 | 3.4439 | 0.4049 | 0.1512 | 0.4049 |
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+ | 2.7312 | 0.21 | 200 | 2.5762 | 0.5766 | 0.3025 | 0.5766 |
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+ | 2.1872 | 0.32 | 300 | 2.0346 | 0.656 | 0.3994 | 0.656 |
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+ | 1.7869 | 0.43 | 400 | 1.6759 | 0.7075 | 0.4796 | 0.7075 |
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+ | 1.5593 | 0.53 | 500 | 1.4354 | 0.7454 | 0.5447 | 0.7454 |
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+ | 1.388 | 0.64 | 600 | 1.2759 | 0.7695 | 0.5778 | 0.7695 |
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+ | 1.214 | 0.75 | 700 | 1.1428 | 0.7806 | 0.5891 | 0.7806 |
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+ | 1.158 | 0.85 | 800 | 1.0531 | 0.784 | 0.5955 | 0.784 |
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+ | 1.0284 | 0.96 | 900 | 0.9726 | 0.7944 | 0.6182 | 0.7944 |
67
+ | 0.9249 | 1.07 | 1000 | 0.9276 | 0.8009 | 0.6295 | 0.8009 |
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+ | 0.9046 | 1.17 | 1100 | 0.8824 | 0.8058 | 0.6413 | 0.8058 |
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+ | 0.9312 | 1.28 | 1200 | 0.8425 | 0.8081 | 0.6450 | 0.8081 |
70
+ | 0.8329 | 1.39 | 1300 | 0.8096 | 0.8135 | 0.6585 | 0.8135 |
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+ | 0.7601 | 1.49 | 1400 | 0.7946 | 0.8148 | 0.6646 | 0.8148 |
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+ | 0.7812 | 1.6 | 1500 | 0.7766 | 0.8192 | 0.6739 | 0.8192 |
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+ | 0.7944 | 1.71 | 1600 | 0.7585 | 0.8221 | 0.6800 | 0.8221 |
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+ | 0.7632 | 1.81 | 1700 | 0.7363 | 0.8269 | 0.6902 | 0.8269 |
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+ | 0.7027 | 1.92 | 1800 | 0.7229 | 0.8227 | 0.6793 | 0.8227 |
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+ | 0.671 | 2.03 | 1900 | 0.7145 | 0.8263 | 0.6870 | 0.8263 |
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+ | 0.6361 | 2.13 | 2000 | 0.7067 | 0.8277 | 0.6952 | 0.8277 |
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+ | 0.6615 | 2.24 | 2100 | 0.6969 | 0.8281 | 0.6974 | 0.8281 |
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+ | 0.6432 | 2.35 | 2200 | 0.6908 | 0.8311 | 0.7054 | 0.8311 |
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+ | 0.648 | 2.45 | 2300 | 0.6850 | 0.8304 | 0.7011 | 0.8304 |
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+ | 0.631 | 2.56 | 2400 | 0.6750 | 0.8323 | 0.7063 | 0.8323 |
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+ | 0.575 | 2.67 | 2500 | 0.6718 | 0.8337 | 0.7094 | 0.8337 |
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+ | 0.6444 | 2.77 | 2600 | 0.6701 | 0.8332 | 0.7102 | 0.8332 |
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+ | 0.6054 | 2.88 | 2700 | 0.6690 | 0.8346 | 0.7122 | 0.8346 |
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+ | 0.6123 | 2.99 | 2800 | 0.6676 | 0.8349 | 0.7127 | 0.8349 |
86
+
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+
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+ ### Framework versions
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+
90
+ - Transformers 4.39.0.dev0
91
+ - Pytorch 2.2.1+cu121
92
+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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+ "train_samples_per_second": 219.74,
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