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--- |
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license: mit |
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base_model: BAAI/bge-base-en-v1.5 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: IKI-Category-multilabel_bge |
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results: [] |
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co2_eq_emissions: |
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emissions: 47.3697569372214 |
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source: codecarbon |
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training_type: fine-tuning |
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on_cloud: false |
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cpu_model: Intel(R) Xeon(R) CPU @ 2.30GHz |
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ram_total_size: 12.674781799316406 |
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hours_used: 0.996 |
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hardware_used: 1 x Tesla T4 |
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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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# IKI-Category-multilabel_bge |
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This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4541 |
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- Precision-micro: 0.75 |
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- Precision-samples: 0.7708 |
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- Precision-weighted: 0.7517 |
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- Recall-micro: 0.7880 |
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- Recall-samples: 0.7858 |
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- Recall-weighted: 0.7880 |
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- F1-micro: 0.7685 |
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- F1-samples: 0.7537 |
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- F1-weighted: 0.7615 |
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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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- learning_rate: 4.5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision-micro | Precision-samples | Precision-weighted | Recall-micro | Recall-samples | Recall-weighted | F1-micro | F1-samples | F1-weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:| |
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| 0.8999 | 0.99 | 94 | 0.8742 | 0.3889 | 0.0272 | 0.1308 | 0.0169 | 0.0188 | 0.0169 | 0.0323 | 0.0202 | 0.0280 | |
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| 0.7377 | 2.0 | 189 | 0.6770 | 0.4727 | 0.4996 | 0.5333 | 0.5639 | 0.5782 | 0.5639 | 0.5143 | 0.4883 | 0.4998 | |
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| 0.5582 | 2.99 | 283 | 0.5552 | 0.5111 | 0.5585 | 0.5685 | 0.7229 | 0.7357 | 0.7229 | 0.5988 | 0.5959 | 0.6175 | |
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| 0.3943 | 4.0 | 378 | 0.4713 | 0.5616 | 0.6397 | 0.5869 | 0.7904 | 0.8071 | 0.7904 | 0.6567 | 0.6761 | 0.6611 | |
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| 0.2883 | 4.99 | 472 | 0.4555 | 0.6384 | 0.6969 | 0.6444 | 0.7446 | 0.7641 | 0.7446 | 0.6874 | 0.6901 | 0.6854 | |
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| 0.2112 | 6.0 | 567 | 0.4459 | 0.6443 | 0.6968 | 0.6637 | 0.7855 | 0.7942 | 0.7855 | 0.7079 | 0.7123 | 0.7068 | |
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| 0.1608 | 6.99 | 661 | 0.4212 | 0.6508 | 0.7071 | 0.6586 | 0.7904 | 0.7931 | 0.7904 | 0.7138 | 0.7161 | 0.7116 | |
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| 0.1247 | 8.0 | 756 | 0.4177 | 0.6633 | 0.7145 | 0.6650 | 0.7976 | 0.8006 | 0.7976 | 0.7243 | 0.7193 | 0.7195 | |
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| 0.1031 | 8.99 | 850 | 0.4435 | 0.7277 | 0.7523 | 0.7306 | 0.7855 | 0.7875 | 0.7855 | 0.7555 | 0.7425 | 0.7487 | |
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| 0.0851 | 10.0 | 945 | 0.4522 | 0.7380 | 0.7623 | 0.7465 | 0.7807 | 0.7795 | 0.7807 | 0.7588 | 0.7432 | 0.7516 | |
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| 0.074 | 10.99 | 1039 | 0.4548 | 0.7359 | 0.7663 | 0.7368 | 0.7855 | 0.7910 | 0.7855 | 0.7599 | 0.7490 | 0.7521 | |
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| 0.0648 | 12.0 | 1134 | 0.4430 | 0.7425 | 0.7676 | 0.7437 | 0.7783 | 0.7781 | 0.7783 | 0.76 | 0.7461 | 0.7540 | |
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| 0.0605 | 12.99 | 1228 | 0.4478 | 0.7366 | 0.7651 | 0.7379 | 0.7952 | 0.7948 | 0.7952 | 0.7648 | 0.7545 | 0.7579 | |
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| 0.0566 | 14.0 | 1323 | 0.4574 | 0.7506 | 0.7708 | 0.7519 | 0.7904 | 0.7893 | 0.7904 | 0.7700 | 0.7546 | 0.7625 | |
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| 0.0546 | 14.92 | 1410 | 0.4541 | 0.75 | 0.7708 | 0.7517 | 0.7880 | 0.7858 | 0.7880 | 0.7685 | 0.7537 | 0.7615 | |
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| Category | Precision | Recall | F1 | Suport | |
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|:-------------------------------------------:|:---------:|:------:|:----:|:-----------:| |
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|Active mobility |0.70 |0.894 |0.7908| 19.0 | |
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|Alternative fuels |0.804 | 0.865 |0.833 | 52.0 | |
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|Aviation improvements |0.700 | 1.00 |0.824 | 7.0 | |
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|Comprehensive transport planning |0.750 | 0.571 |0.649 | 21.0 | |
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|Digital solutions | 0.708 | 0.772 |0.739 | 22.0 | |
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|Economic instruments |0.742 | 0.821 |0.780 | 28.0 | |
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|Education and behavioral change |0.727 | 0.727 |0.727 | 11.0 | |
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|Electric mobility |0.766 | 0.922 |0.837 | 64.0 | |
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|Freight efficiency improvements |0.768 |0.650 |0.703 | 20.0 | |
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|Improve infrastructure |0.638 | 0.857 |0.732 | 35.0 | |
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|Land use |1.00 | 0.625 |0.769 | 8.0 | |
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|Other Transport Category |0.600 | 0.27 |0.375 | 11.0 | |
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|Public transport improvement |0.777 | 0.833 |0.804 | 42.0 | |
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|Shipping improvements |0.846 | 0.846 |0.846 | 13.0 | |
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|Transport demand management |0.666 |0.40 |0.500 | 15.0 | |
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|Vehicle improvements |0.783 | 0.766 |0.774 | 47.0 | |
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### Environmental Impact |
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Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
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- **Carbon Emitted**: 0.0473 kg of CO2 |
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- **Hours Used**: 0.996 hours |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x Tesla T4 |
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- **CPU Model**: Intel(R) Xeon(R) CPU @ 2.30GHz |
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- **RAM Size**: 12.67 GB |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.1 |
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