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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-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: distilbert-base-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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# distilbert-base-uncased-finetuned-ner-harem |
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2870 |
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- Precision: 0.6543 |
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- Recall: 0.6256 |
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- F1: 0.6397 |
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- Accuracy: 0.9433 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 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 | 1.0 | 282 | 0.3862 | 0.4247 | 0.2901 | 0.3447 | 0.9041 | |
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| 0.4179 | 2.0 | 564 | 0.3053 | 0.5411 | 0.4216 | 0.4739 | 0.9228 | |
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| 0.4179 | 3.0 | 846 | 0.2923 | 0.6195 | 0.5025 | 0.5549 | 0.9315 | |
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| 0.2108 | 4.0 | 1128 | 0.2770 | 0.5641 | 0.5346 | 0.5489 | 0.9322 | |
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| 0.2108 | 5.0 | 1410 | 0.2789 | 0.6104 | 0.5548 | 0.5813 | 0.9369 | |
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| 0.1279 | 6.0 | 1692 | 0.2793 | 0.6171 | 0.5953 | 0.6060 | 0.9382 | |
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| 0.1279 | 7.0 | 1974 | 0.2790 | 0.6348 | 0.6037 | 0.6188 | 0.9417 | |
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| 0.0881 | 8.0 | 2256 | 0.2864 | 0.6490 | 0.6206 | 0.6345 | 0.9419 | |
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| 0.0615 | 9.0 | 2538 | 0.2874 | 0.6414 | 0.6003 | 0.6202 | 0.9417 | |
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| 0.0615 | 10.0 | 2820 | 0.2870 | 0.6543 | 0.6256 | 0.6397 | 0.9433 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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