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--- |
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license: apache-2.0 |
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base_model: bert-base-multilingual-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: multibertfinetuned2209 |
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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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# multibertfinetuned2209 |
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This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3973 |
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- Precision: 0.7567 |
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- Recall: 0.7607 |
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- F1: 0.7587 |
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- Accuracy: 0.9064 |
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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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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- num_epochs: 8 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 118 | 0.4058 | 0.7597 | 0.7343 | 0.7468 | 0.9032 | |
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| No log | 2.0 | 236 | 0.3973 | 0.7567 | 0.7607 | 0.7587 | 0.9064 | |
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| No log | 3.0 | 354 | 0.4153 | 0.7540 | 0.7677 | 0.7608 | 0.9062 | |
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| No log | 4.0 | 472 | 0.4656 | 0.7645 | 0.7466 | 0.7555 | 0.9082 | |
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| 0.0692 | 5.0 | 590 | 0.4940 | 0.7594 | 0.7554 | 0.7574 | 0.9043 | |
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| 0.0692 | 6.0 | 708 | 0.5446 | 0.7668 | 0.7484 | 0.7575 | 0.9059 | |
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| 0.0692 | 7.0 | 826 | 0.5732 | 0.7818 | 0.7420 | 0.7613 | 0.9069 | |
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| 0.0692 | 8.0 | 944 | 0.5668 | 0.7844 | 0.7431 | 0.7632 | 0.9082 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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