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license: apache-2.0 |
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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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base_model: google/electra-base-discriminator |
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model-index: |
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- name: electra-base-discriminator-ner-food-combined-v2 |
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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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# electra-base-discriminator-ner-food-combined-v2 |
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This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1277 |
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- Precision: 0.8006 |
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- Recall: 0.8959 |
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- F1: 0.8456 |
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- Accuracy: 0.9685 |
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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-06 |
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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: 7 |
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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 | 0.45 | 400 | 0.1279 | 0.7429 | 0.8888 | 0.8093 | 0.9603 | |
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| 0.2005 | 0.9 | 800 | 0.1306 | 0.8145 | 0.8901 | 0.8506 | 0.9704 | |
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| 0.1305 | 1.35 | 1200 | 0.1197 | 0.7847 | 0.8951 | 0.8363 | 0.9667 | |
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| 0.1143 | 1.8 | 1600 | 0.1118 | 0.7876 | 0.8922 | 0.8366 | 0.9661 | |
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| 0.1169 | 2.25 | 2000 | 0.1125 | 0.7724 | 0.8959 | 0.8296 | 0.9647 | |
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| 0.1169 | 2.7 | 2400 | 0.1167 | 0.7964 | 0.8922 | 0.8415 | 0.9674 | |
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| 0.1007 | 3.15 | 2800 | 0.1222 | 0.8170 | 0.8905 | 0.8522 | 0.9708 | |
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| 0.1008 | 3.6 | 3200 | 0.1164 | 0.7732 | 0.8913 | 0.8281 | 0.9640 | |
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| 0.0973 | 4.04 | 3600 | 0.1190 | 0.8093 | 0.8993 | 0.8519 | 0.9697 | |
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| 0.0948 | 4.49 | 4000 | 0.1221 | 0.7977 | 0.8947 | 0.8434 | 0.9676 | |
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| 0.0948 | 4.94 | 4400 | 0.1220 | 0.8009 | 0.8993 | 0.8472 | 0.9684 | |
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| 0.0857 | 5.39 | 4800 | 0.1292 | 0.8085 | 0.8963 | 0.8501 | 0.9694 | |
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| 0.0845 | 5.84 | 5200 | 0.1318 | 0.8236 | 0.8943 | 0.8575 | 0.9710 | |
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| 0.0877 | 6.29 | 5600 | 0.1246 | 0.7940 | 0.8972 | 0.8425 | 0.9674 | |
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| 0.0825 | 6.74 | 6000 | 0.1277 | 0.8006 | 0.8959 | 0.8456 | 0.9685 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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