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--- |
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license: apache-2.0 |
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base_model: distilbert-base-multilingual-cased |
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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: distilbert-base-multilingual-cased-language-detection-fp16-false-bs-64 |
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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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# distilbert-base-multilingual-cased-language-detection-fp16-false-bs-64 |
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This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0103 |
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- Accuracy: 0.9992 |
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- Weighted f1: 0.9992 |
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- Micro f1: 0.9992 |
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- Macro f1: 0.9992 |
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- Weighted recall: 0.9992 |
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- Micro recall: 0.9992 |
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- Macro recall: 0.9992 |
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- Weighted precision: 0.9992 |
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- Micro precision: 0.9992 |
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- Macro precision: 0.9992 |
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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: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 0.1689 | 1.0 | 165 | 0.0103 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | |
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| 0.0096 | 2.0 | 330 | 0.0115 | 0.9977 | 0.9977 | 0.9977 | 0.9977 | 0.9977 | 0.9977 | 0.9977 | 0.9977 | 0.9977 | 0.9977 | |
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| 0.0027 | 3.0 | 495 | 0.0032 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | |
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| 0.0011 | 4.0 | 660 | 0.0022 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | |
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| 0.0007 | 5.0 | 825 | 0.0027 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | 0.9992 | |
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### Framework versions |
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- Transformers 4.33.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4.dev0 |
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- Tokenizers 0.13.3 |
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