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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-large-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: bert-large-uncased-finetuned-ner-harem |
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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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# bert-large-uncased-finetuned-ner-harem |
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This model is a fine-tuned version of [google-bert/bert-large-uncased](https://huggingface.co./google-bert/bert-large-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3109 |
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- Precision: 0.6895 |
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- Recall: 0.6442 |
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- F1: 0.6661 |
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- Accuracy: 0.9512 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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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 | 0.9978 | 281 | 0.2896 | 0.5442 | 0.4772 | 0.5085 | 0.9238 | |
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| 0.3496 | 1.9973 | 562 | 0.2340 | 0.6811 | 0.5295 | 0.5958 | 0.9412 | |
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| 0.3496 | 2.9969 | 843 | 0.2240 | 0.5876 | 0.5599 | 0.5734 | 0.9409 | |
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| 0.1372 | 3.9964 | 1124 | 0.2540 | 0.6910 | 0.6223 | 0.6548 | 0.9403 | |
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| 0.1372 | 4.9960 | 1405 | 0.2598 | 0.6433 | 0.6358 | 0.6395 | 0.9439 | |
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| 0.0648 | 5.9956 | 1686 | 0.2377 | 0.6945 | 0.6442 | 0.6684 | 0.9497 | |
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| 0.0648 | 6.9951 | 1967 | 0.2822 | 0.6965 | 0.6425 | 0.6684 | 0.9501 | |
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| 0.0316 | 7.9982 | 2249 | 0.2958 | 0.7044 | 0.6509 | 0.6766 | 0.9518 | |
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| 0.0148 | 8.9978 | 2530 | 0.3006 | 0.6944 | 0.6476 | 0.6702 | 0.9496 | |
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| 0.0148 | 9.9938 | 2810 | 0.3109 | 0.6895 | 0.6442 | 0.6661 | 0.9512 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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