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--- |
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license: mit |
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base_model: deepset/gbert-large |
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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: gbert-large_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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# gbert-large_ner |
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This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3755 |
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- Precision: 0.9010 |
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- Recall: 0.8948 |
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- F1: 0.8975 |
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- Accuracy: 0.9521 |
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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: 16 |
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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: 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 | 438 | 0.2334 | 0.8727 | 0.8653 | 0.8649 | 0.9303 | |
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| 0.3598 | 2.0 | 876 | 0.2149 | 0.8885 | 0.8649 | 0.8757 | 0.9391 | |
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| 0.1678 | 3.0 | 1314 | 0.2257 | 0.8820 | 0.8906 | 0.8847 | 0.9461 | |
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| 0.1054 | 4.0 | 1752 | 0.2580 | 0.8902 | 0.8884 | 0.8884 | 0.9463 | |
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| 0.0645 | 5.0 | 2190 | 0.2881 | 0.8896 | 0.8820 | 0.8833 | 0.9451 | |
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| 0.0436 | 6.0 | 2628 | 0.2767 | 0.8922 | 0.8911 | 0.8914 | 0.9479 | |
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| 0.0245 | 7.0 | 3066 | 0.3190 | 0.9026 | 0.9038 | 0.9030 | 0.9534 | |
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| 0.0108 | 8.0 | 3504 | 0.3547 | 0.8879 | 0.8886 | 0.8876 | 0.9474 | |
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| 0.0108 | 9.0 | 3942 | 0.3780 | 0.8943 | 0.8886 | 0.8910 | 0.9494 | |
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| 0.0074 | 10.0 | 4380 | 0.3755 | 0.9010 | 0.8948 | 0.8975 | 0.9521 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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