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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: MiguelCosta/distilbert-1-finetuned-cisco |
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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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# MiguelCosta/distilbert-1-finetuned-cisco |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 2.2723 |
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- Validation Loss: 2.4284 |
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- Epoch: 39 |
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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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -964, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 4.4357 | 4.3213 | 0 | |
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| 4.1763 | 3.9111 | 1 | |
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| 3.8803 | 3.6751 | 2 | |
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| 3.7135 | 3.5458 | 3 | |
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| 3.5861 | 3.4489 | 4 | |
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| 3.5176 | 3.4323 | 5 | |
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| 3.4022 | 3.3658 | 6 | |
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| 3.3259 | 3.2113 | 7 | |
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| 3.2499 | 3.0623 | 8 | |
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| 3.2129 | 3.0298 | 9 | |
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| 3.1177 | 2.9181 | 10 | |
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| 3.0144 | 2.9550 | 11 | |
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| 2.9502 | 2.8758 | 12 | |
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| 2.9074 | 2.8674 | 13 | |
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| 2.8922 | 2.7877 | 14 | |
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| 2.8333 | 2.8283 | 15 | |
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| 2.7982 | 2.7717 | 16 | |
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| 2.7453 | 2.7578 | 17 | |
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| 2.6611 | 2.5425 | 18 | |
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| 2.6330 | 2.6145 | 19 | |
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| 2.5642 | 2.5415 | 20 | |
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| 2.5352 | 2.5437 | 21 | |
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| 2.4939 | 2.4214 | 22 | |
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| 2.4287 | 2.4882 | 23 | |
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| 2.4142 | 2.5091 | 24 | |
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| 2.3676 | 2.3997 | 25 | |
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| 2.3121 | 2.4515 | 26 | |
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| 2.3085 | 2.2349 | 27 | |
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| 2.2839 | 2.3205 | 28 | |
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| 2.3248 | 2.3273 | 29 | |
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| 2.2763 | 2.2583 | 30 | |
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| 2.2710 | 2.3896 | 31 | |
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| 2.2950 | 2.3224 | 32 | |
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| 2.3026 | 2.3910 | 33 | |
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| 2.3116 | 2.3255 | 34 | |
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| 2.2640 | 2.3186 | 35 | |
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| 2.2958 | 2.3332 | 36 | |
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| 2.3256 | 2.3646 | 37 | |
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| 2.2831 | 2.3751 | 38 | |
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| 2.2723 | 2.4284 | 39 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- TensorFlow 2.8.2 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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