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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-cased |
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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: mbert-Nepali-NER |
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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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# mbert-Nepali-NER |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2462 |
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- Precision: 0.3727 |
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- Recall: 0.3154 |
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- F1: 0.3417 |
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- Accuracy: 0.9555 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.3162 | 0.29 | 500 | 0.2577 | 0.2647 | 0.0692 | 0.1098 | 0.9303 | |
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| 0.205 | 0.58 | 1000 | 0.2505 | 0.5312 | 0.1308 | 0.2099 | 0.9378 | |
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| 0.176 | 0.87 | 1500 | 0.2241 | 0.3372 | 0.2231 | 0.2685 | 0.9434 | |
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| 0.1364 | 1.16 | 2000 | 0.2304 | 0.3125 | 0.1923 | 0.2381 | 0.9473 | |
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| 0.1188 | 1.44 | 2500 | 0.2136 | 0.25 | 0.3385 | 0.2876 | 0.9413 | |
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| 0.1056 | 1.73 | 3000 | 0.2134 | 0.3462 | 0.2769 | 0.3077 | 0.9517 | |
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| 0.1002 | 2.02 | 3500 | 0.2207 | 0.2632 | 0.3077 | 0.2837 | 0.9528 | |
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| 0.0695 | 2.31 | 4000 | 0.2153 | 0.3041 | 0.3462 | 0.3237 | 0.9524 | |
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| 0.0701 | 2.6 | 4500 | 0.2038 | 0.2674 | 0.3538 | 0.3046 | 0.9545 | |
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| 0.0649 | 2.89 | 5000 | 0.2090 | 0.2848 | 0.3462 | 0.3125 | 0.9536 | |
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| 0.0533 | 3.18 | 5500 | 0.2341 | 0.3913 | 0.2769 | 0.3243 | 0.9546 | |
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| 0.0422 | 3.47 | 6000 | 0.2459 | 0.4545 | 0.3077 | 0.3670 | 0.9537 | |
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| 0.0396 | 3.76 | 6500 | 0.2389 | 0.3846 | 0.3077 | 0.3419 | 0.9540 | |
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| 0.0376 | 4.04 | 7000 | 0.2296 | 0.4091 | 0.2769 | 0.3303 | 0.9550 | |
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| 0.0221 | 4.33 | 7500 | 0.2533 | 0.3962 | 0.3231 | 0.3559 | 0.9555 | |
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| 0.021 | 4.62 | 8000 | 0.2609 | 0.3922 | 0.3077 | 0.3448 | 0.9548 | |
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| 0.0251 | 4.91 | 8500 | 0.2462 | 0.3727 | 0.3154 | 0.3417 | 0.9555 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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