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--- |
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license: apache-2.0 |
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base_model: sentence-transformers/paraphrase-multilingual-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: mpnet-multi-agri-classifier |
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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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# mpnet-multi-agri-classifier |
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This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-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: 0.3444 |
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- Precision Macro: 0.8402 |
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- Precision Weighted: 0.9196 |
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- Recall Macro: 0.9042 |
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- Recall Weighted: 0.9046 |
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- F1-score: 0.8655 |
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- Accuracy: 0.9046 |
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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: 6.9e-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: 200 |
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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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| 0.393 | 1.0 | 334 | 0.2938 | 0.8528 | 0.9132 | 0.8773 | 0.9092 | 0.8641 | 0.9092 | |
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| 0.3213 | 2.0 | 668 | 0.3049 | 0.8299 | 0.9110 | 0.8885 | 0.8966 | 0.8534 | 0.8966 | |
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| 0.2591 | 3.0 | 1002 | 0.2561 | 0.8654 | 0.9226 | 0.8937 | 0.9184 | 0.8784 | 0.9184 | |
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| 0.1853 | 4.0 | 1336 | 0.3254 | 0.8386 | 0.9190 | 0.9034 | 0.9034 | 0.8641 | 0.9034 | |
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| 0.1119 | 5.0 | 1670 | 0.3444 | 0.8402 | 0.9196 | 0.9042 | 0.9046 | 0.8655 | 0.9046 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 1.12.0+cu102 |
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- Datasets 2.3.2 |
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- Tokenizers 0.15.0 |
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