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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-small-finetuned-ner-to-multilabel-finer-139 |
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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-small-finetuned-ner-to-multilabel-finer-139 |
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This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0019 |
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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: 3e-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: constant |
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- num_epochs: 40 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.1398 | 0.0 | 500 | 0.0244 | |
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| 0.0164 | 0.01 | 1000 | 0.0114 | |
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| 0.01 | 0.01 | 1500 | 0.0084 | |
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| 0.0081 | 0.02 | 2000 | 0.0073 | |
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| 0.0072 | 0.02 | 2500 | 0.0068 | |
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| 0.0069 | 0.03 | 3000 | 0.0065 | |
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| 0.0067 | 0.03 | 3500 | 0.0063 | |
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| 0.0066 | 0.04 | 4000 | 0.0062 | |
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| 0.0061 | 0.04 | 4500 | 0.0062 | |
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| 0.0069 | 0.04 | 5000 | 0.0061 | |
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| 0.0063 | 0.05 | 5500 | 0.0061 | |
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| 0.0062 | 0.05 | 6000 | 0.0061 | |
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| 0.006 | 0.06 | 6500 | 0.0061 | |
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| 0.0059 | 0.06 | 7000 | 0.0056 | |
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| 0.0058 | 0.07 | 7500 | 0.0054 | |
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| 0.0054 | 0.07 | 8000 | 0.0054 | |
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| 0.0057 | 0.08 | 8500 | 0.0053 | |
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| 0.0057 | 0.08 | 9000 | 0.0052 | |
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| 0.0056 | 0.08 | 9500 | 0.0051 | |
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| 0.0051 | 0.09 | 10000 | 0.0050 | |
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| 0.0054 | 0.09 | 10500 | 0.0049 | |
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| 0.005 | 0.1 | 11000 | 0.0048 | |
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| 0.0049 | 0.1 | 11500 | 0.0046 | |
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| 0.0049 | 0.11 | 12000 | 0.0046 | |
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| 0.0046 | 0.11 | 12500 | 0.0044 | |
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| 0.0043 | 0.12 | 13000 | 0.0043 | |
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| 0.0045 | 0.12 | 13500 | 0.0042 | |
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| 0.0042 | 0.12 | 14000 | 0.0042 | |
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| 0.0042 | 0.13 | 14500 | 0.0039 | |
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| 0.0042 | 0.13 | 15000 | 0.0038 | |
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| 0.0039 | 0.14 | 15500 | 0.0037 | |
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| 0.004 | 0.14 | 16000 | 0.0036 | |
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| 0.0037 | 0.15 | 16500 | 0.0035 | |
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| 0.0036 | 0.15 | 17000 | 0.0035 | |
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| 0.0036 | 0.16 | 17500 | 0.0035 | |
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| 0.0035 | 0.16 | 18000 | 0.0033 | |
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| 0.0037 | 0.16 | 18500 | 0.0033 | |
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| 0.0035 | 0.17 | 19000 | 0.0032 | |
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| 0.0032 | 0.17 | 19500 | 0.0031 | |
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| 0.0032 | 0.18 | 20000 | 0.0031 | |
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| 0.0033 | 0.18 | 20500 | 0.0030 | |
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| 0.003 | 0.19 | 21000 | 0.0030 | |
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| 0.0034 | 0.19 | 21500 | 0.0029 | |
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| 0.0031 | 0.2 | 22000 | 0.0029 | |
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| 0.003 | 0.2 | 22500 | 0.0028 | |
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| 0.0032 | 0.2 | 23000 | 0.0028 | |
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| 0.003 | 0.21 | 23500 | 0.0027 | |
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| 0.0029 | 0.21 | 24000 | 0.0027 | |
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| 0.0027 | 0.22 | 24500 | 0.0026 | |
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| 0.0029 | 0.22 | 25000 | 0.0026 | |
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| 0.0027 | 0.23 | 25500 | 0.0026 | |
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| 0.0028 | 0.23 | 26000 | 0.0026 | |
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| 0.0027 | 0.24 | 26500 | 0.0025 | |
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| 0.0026 | 0.24 | 27000 | 0.0025 | |
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| 0.0026 | 0.24 | 27500 | 0.0025 | |
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| 0.0026 | 0.25 | 28000 | 0.0024 | |
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| 0.0025 | 0.25 | 28500 | 0.0024 | |
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| 0.0026 | 0.26 | 29000 | 0.0024 | |
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| 0.0025 | 0.26 | 29500 | 0.0024 | |
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| 0.0024 | 0.27 | 30000 | 0.0024 | |
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| 0.0026 | 0.27 | 30500 | 0.0023 | |
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| 0.0024 | 0.28 | 31000 | 0.0023 | |
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| 0.0025 | 0.28 | 31500 | 0.0023 | |
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| 0.0024 | 0.28 | 32000 | 0.0023 | |
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| 0.0023 | 0.29 | 32500 | 0.0022 | |
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| 0.0024 | 0.29 | 33000 | 0.0022 | |
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| 0.0024 | 0.3 | 33500 | 0.0022 | |
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| 0.0022 | 0.3 | 34000 | 0.0022 | |
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| 0.0023 | 0.31 | 34500 | 0.0021 | |
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| 0.0023 | 0.31 | 35000 | 0.0021 | |
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| 0.0024 | 0.32 | 35500 | 0.0021 | |
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| 0.0023 | 0.32 | 36000 | 0.0021 | |
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| 0.0023 | 0.32 | 36500 | 0.0021 | |
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| 0.0021 | 0.33 | 37000 | 0.0021 | |
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| 0.0021 | 0.33 | 37500 | 0.0021 | |
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| 0.0022 | 0.34 | 38000 | 0.0021 | |
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| 0.0022 | 0.34 | 38500 | 0.0020 | |
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| 0.0022 | 0.35 | 39000 | 0.0020 | |
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| 0.0022 | 0.35 | 39500 | 0.0020 | |
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| 0.0022 | 0.36 | 40000 | 0.0022 | |
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| 0.0022 | 0.36 | 40500 | 0.0020 | |
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| 0.0022 | 0.36 | 41000 | 0.0020 | |
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| 0.0021 | 0.37 | 41500 | 0.0020 | |
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| 0.0022 | 0.37 | 42000 | 0.0020 | |
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| 0.0021 | 0.38 | 42500 | 0.0020 | |
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| 0.0021 | 0.38 | 43000 | 0.0019 | |
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| 0.0022 | 0.39 | 43500 | 0.0019 | |
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| 0.002 | 0.39 | 44000 | 0.0019 | |
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| 0.0021 | 0.4 | 44500 | 0.0020 | |
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| 0.0022 | 0.4 | 45000 | 0.0019 | |
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| 0.0022 | 0.4 | 45500 | 0.0019 | |
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| 0.002 | 0.41 | 46000 | 0.0019 | |
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| 0.0018 | 0.41 | 46500 | 0.0019 | |
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| 0.0022 | 0.42 | 47000 | 0.0019 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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