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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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- recall |
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- accuracy |
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model-index: |
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- name: multibert_1210seed25 |
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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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# multibert_1210seed25 |
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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: 0.4453 |
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- Precisions: 0.8647 |
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- Recall: 0.8314 |
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- F-measure: 0.8459 |
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- Accuracy: 0.9141 |
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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: 7.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 25 |
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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: 14 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
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| 0.6013 | 1.0 | 236 | 0.4080 | 0.8974 | 0.6857 | 0.7273 | 0.8736 | |
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| 0.319 | 2.0 | 472 | 0.3621 | 0.8338 | 0.7306 | 0.7317 | 0.8875 | |
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| 0.1929 | 3.0 | 708 | 0.3823 | 0.8020 | 0.7680 | 0.7761 | 0.9022 | |
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| 0.1389 | 4.0 | 944 | 0.4353 | 0.8400 | 0.7742 | 0.7990 | 0.9003 | |
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| 0.0958 | 5.0 | 1180 | 0.4348 | 0.8726 | 0.7547 | 0.7971 | 0.9011 | |
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| 0.0676 | 6.0 | 1416 | 0.4453 | 0.8647 | 0.8314 | 0.8459 | 0.9141 | |
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| 0.0506 | 7.0 | 1652 | 0.5222 | 0.8555 | 0.8013 | 0.8253 | 0.9100 | |
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| 0.0315 | 8.0 | 1888 | 0.5192 | 0.8700 | 0.7873 | 0.8187 | 0.9108 | |
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| 0.0229 | 9.0 | 2124 | 0.5977 | 0.8402 | 0.7839 | 0.8079 | 0.9062 | |
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| 0.0149 | 10.0 | 2360 | 0.6061 | 0.8622 | 0.8069 | 0.8305 | 0.9131 | |
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| 0.0122 | 11.0 | 2596 | 0.5894 | 0.8419 | 0.7702 | 0.7983 | 0.9085 | |
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| 0.0065 | 12.0 | 2832 | 0.6120 | 0.8514 | 0.7700 | 0.8021 | 0.9089 | |
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| 0.0039 | 13.0 | 3068 | 0.6434 | 0.8437 | 0.7646 | 0.7965 | 0.9055 | |
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| 0.003 | 14.0 | 3304 | 0.6391 | 0.8403 | 0.7670 | 0.7973 | 0.9062 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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