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--- |
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license: cc-by-4.0 |
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base_model: deepset/roberta-large-squad2 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: roberta-large-squad2-finetuned-dtc |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# roberta-large-squad2-finetuned-dtc |
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This model is a fine-tuned version of [deepset/roberta-large-squad2](https://huggingface.co/deepset/roberta-large-squad2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 1.9389 |
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- Train End Logits Loss: 1.1432 |
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- Train Start Logits Loss: 0.7957 |
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- Train End Logits Acc: 0.7392 |
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- Train Start Logits Acc: 0.8093 |
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- Validation Loss: 3.7259 |
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- Validation End Logits Loss: 1.8885 |
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- Validation Start Logits Loss: 1.8374 |
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- Validation End Logits Acc: 0.6312 |
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- Validation Start Logits Acc: 0.7221 |
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- Epoch: 36 |
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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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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2.4e-05, 'decay_steps': 21400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.03} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Train End Logits Loss | Train Start Logits Loss | Train End Logits Acc | Train Start Logits Acc | Validation Loss | Validation End Logits Loss | Validation Start Logits Loss | Validation End Logits Acc | Validation Start Logits Acc | Epoch | |
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|:----------:|:---------------------:|:-----------------------:|:--------------------:|:----------------------:|:---------------:|:--------------------------:|:----------------------------:|:-------------------------:|:---------------------------:|:-----:| |
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| 5.8888 | 3.0592 | 2.8296 | 0.5456 | 0.5406 | 4.8715 | 2.6861 | 2.1854 | 0.6130 | 0.6182 | 0 | |
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| 5.0000 | 2.7063 | 2.2937 | 0.5809 | 0.5762 | 4.0680 | 2.3593 | 1.7087 | 0.6208 | 0.6000 | 1 | |
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| 4.7529 | 2.5952 | 2.1576 | 0.5929 | 0.5862 | 4.5767 | 2.7450 | 1.8317 | 0.6208 | 0.6156 | 2 | |
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| 4.6181 | 2.5511 | 2.0670 | 0.5984 | 0.5873 | 3.9828 | 2.4125 | 1.5703 | 0.6208 | 0.6052 | 3 | |
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| 4.4766 | 2.4920 | 1.9846 | 0.6019 | 0.5946 | 3.7862 | 2.2460 | 1.5402 | 0.6208 | 0.5922 | 4 | |
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| 4.5692 | 2.5720 | 1.9972 | 0.6081 | 0.6066 | 3.6069 | 2.1558 | 1.4511 | 0.6208 | 0.6052 | 5 | |
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| 4.3098 | 2.4382 | 1.8716 | 0.6016 | 0.5987 | 3.2741 | 1.9275 | 1.3466 | 0.6208 | 0.6364 | 6 | |
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| 3.8906 | 2.2240 | 1.6666 | 0.6165 | 0.6256 | 3.3856 | 1.9718 | 1.4138 | 0.6156 | 0.6052 | 7 | |
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| 3.7711 | 2.1773 | 1.5939 | 0.6154 | 0.6317 | 3.4381 | 1.7916 | 1.6465 | 0.6182 | 0.4805 | 8 | |
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| 3.6331 | 2.1149 | 1.5182 | 0.6177 | 0.6460 | 3.0055 | 1.6855 | 1.3200 | 0.5273 | 0.6338 | 9 | |
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| 3.4683 | 2.0212 | 1.4471 | 0.6168 | 0.6545 | 3.3422 | 1.7875 | 1.5547 | 0.4805 | 0.5325 | 10 | |
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| 3.3695 | 1.9567 | 1.4129 | 0.6183 | 0.6618 | 2.8283 | 1.5488 | 1.2795 | 0.5455 | 0.6286 | 11 | |
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| 3.3125 | 1.9344 | 1.3781 | 0.6215 | 0.6647 | 2.7086 | 1.5124 | 1.1962 | 0.5636 | 0.6338 | 12 | |
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| 3.2580 | 1.9282 | 1.3298 | 0.6390 | 0.6852 | 3.0502 | 1.7520 | 1.2982 | 0.6156 | 0.6623 | 13 | |
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| 3.2814 | 1.9478 | 1.3336 | 0.6294 | 0.6711 | 2.5437 | 1.4591 | 1.0846 | 0.5948 | 0.6727 | 14 | |
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| 3.1027 | 1.8305 | 1.2721 | 0.6370 | 0.6893 | 3.0537 | 1.6897 | 1.3640 | 0.5481 | 0.5922 | 15 | |
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| 2.7670 | 1.6628 | 1.1042 | 0.6583 | 0.7217 | 2.4372 | 1.3791 | 1.0581 | 0.6519 | 0.6961 | 16 | |
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| 2.7880 | 1.6975 | 1.0905 | 0.6583 | 0.7339 | 2.2441 | 1.2735 | 0.9706 | 0.7039 | 0.7299 | 17 | |
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| 2.7786 | 1.6524 | 1.1262 | 0.6606 | 0.7225 | 2.6408 | 1.4267 | 1.2141 | 0.6701 | 0.6831 | 18 | |
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| 2.4685 | 1.4862 | 0.9823 | 0.6741 | 0.7447 | 2.7726 | 1.5947 | 1.1779 | 0.6338 | 0.6909 | 19 | |
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| 2.4204 | 1.4523 | 0.9682 | 0.6814 | 0.7538 | 2.1115 | 1.1877 | 0.9238 | 0.7429 | 0.7714 | 20 | |
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| 2.2158 | 1.3472 | 0.8686 | 0.6939 | 0.7707 | 2.2647 | 1.2382 | 1.0266 | 0.7143 | 0.7532 | 21 | |
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| 2.0138 | 1.2461 | 0.7676 | 0.7109 | 0.7994 | 2.1425 | 1.1617 | 0.9808 | 0.7455 | 0.7558 | 22 | |
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| 2.0038 | 1.2585 | 0.7453 | 0.7129 | 0.8008 | 1.8952 | 0.9984 | 0.8968 | 0.7688 | 0.7558 | 23 | |
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| 1.8391 | 1.1600 | 0.6791 | 0.7231 | 0.8186 | 2.4242 | 1.3208 | 1.1034 | 0.7013 | 0.7039 | 24 | |
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| 1.7792 | 1.1060 | 0.6732 | 0.7389 | 0.8248 | 1.8800 | 1.0211 | 0.8588 | 0.7792 | 0.7818 | 25 | |
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| 1.6690 | 1.0636 | 0.6054 | 0.7462 | 0.8367 | 2.2503 | 1.2198 | 1.0305 | 0.7325 | 0.7506 | 26 | |
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| 1.6197 | 1.0327 | 0.5870 | 0.7591 | 0.8452 | 1.9393 | 0.9581 | 0.9812 | 0.7974 | 0.8052 | 27 | |
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| 1.5335 | 0.9795 | 0.5540 | 0.7652 | 0.8595 | 2.2046 | 1.1750 | 1.0296 | 0.7688 | 0.7870 | 28 | |
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| 1.4563 | 0.9314 | 0.5249 | 0.7751 | 0.8621 | 1.9638 | 1.0204 | 0.9434 | 0.7403 | 0.7792 | 29 | |
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| 1.3903 | 0.9049 | 0.4854 | 0.7772 | 0.8683 | 2.2657 | 1.1569 | 1.1088 | 0.7636 | 0.7896 | 30 | |
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| 1.3534 | 0.8813 | 0.4720 | 0.7859 | 0.8744 | 1.9620 | 0.9779 | 0.9840 | 0.7688 | 0.7740 | 31 | |
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| 1.4848 | 0.9444 | 0.5405 | 0.7684 | 0.8563 | 2.3368 | 1.1941 | 1.1427 | 0.7299 | 0.7688 | 32 | |
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| 1.5092 | 0.9534 | 0.5558 | 0.7550 | 0.8461 | 2.1233 | 1.0956 | 1.0277 | 0.7610 | 0.7740 | 33 | |
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| 1.4016 | 0.8789 | 0.5227 | 0.7751 | 0.8624 | 2.4886 | 1.2593 | 1.2294 | 0.7403 | 0.7844 | 34 | |
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| 1.8007 | 1.0509 | 0.7498 | 0.7520 | 0.8183 | 2.5730 | 1.3045 | 1.2686 | 0.7195 | 0.7481 | 35 | |
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| 1.9389 | 1.1432 | 0.7957 | 0.7392 | 0.8093 | 3.7259 | 1.8885 | 1.8374 | 0.6312 | 0.7221 | 36 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- TensorFlow 2.14.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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