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--- |
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license: apache-2.0 |
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base_model: sentence-transformers/all-mpnet-base-v2 |
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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: IKT_classifier_economywide_best |
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results: [] |
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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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# IKT_classifier_economywide_best |
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This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1184 |
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- Precision Macro: 0.9615 |
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- Precision Weighted: 0.9630 |
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- Recall Macro: 0.9635 |
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- Recall Weighted: 0.9623 |
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- F1-score: 0.9621 |
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- Accuracy: 0.9623 |
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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: 7.132195091261459e-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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- lr_scheduler_warmup_steps: 300.0 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision Macro | Precision Weighted | Recall Macro | Recall Weighted | F1-score | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:------------:|:---------------:|:--------:|:--------:| |
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| No log | 1.0 | 60 | 0.4189 | 0.9426 | 0.9442 | 0.9445 | 0.9434 | 0.9432 | 0.9434 | |
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| No log | 2.0 | 120 | 0.1438 | 0.9521 | 0.9531 | 0.9533 | 0.9528 | 0.9526 | 0.9528 | |
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| No log | 3.0 | 180 | 0.1119 | 0.9615 | 0.9630 | 0.9635 | 0.9623 | 0.9621 | 0.9623 | |
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| No log | 4.0 | 240 | 0.1477 | 0.9521 | 0.9531 | 0.9533 | 0.9528 | 0.9526 | 0.9528 | |
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| No log | 5.0 | 300 | 0.1184 | 0.9615 | 0.9630 | 0.9635 | 0.9623 | 0.9621 | 0.9623 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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