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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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model-index: |
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- name: distilbert-expense-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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# distilbert-expense-ner |
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This model is a fine-tuned version of [Geotrend/distilbert-base-en-fr-de-no-da-cased](https://huggingface.co/Geotrend/distilbert-base-en-fr-de-no-da-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1641 |
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- Precision: 0.9489 |
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- Recall: 0.9430 |
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- F1: 0.9459 |
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- Accuracy: 0.9721 |
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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: 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: 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 | 112 | 0.1750 | 0.8720 | 0.8891 | 0.8805 | 0.9446 | |
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| No log | 2.0 | 224 | 0.1339 | 0.8804 | 0.9078 | 0.8939 | 0.9515 | |
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| No log | 3.0 | 336 | 0.1157 | 0.9315 | 0.9295 | 0.9305 | 0.9666 | |
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| No log | 4.0 | 448 | 0.1291 | 0.9269 | 0.9326 | 0.9298 | 0.9666 | |
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| 0.2164 | 5.0 | 560 | 0.1400 | 0.9247 | 0.9285 | 0.9266 | 0.9666 | |
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| 0.2164 | 6.0 | 672 | 0.1463 | 0.9376 | 0.9347 | 0.9362 | 0.9689 | |
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| 0.2164 | 7.0 | 784 | 0.1463 | 0.9327 | 0.9337 | 0.9332 | 0.9694 | |
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| 0.2164 | 8.0 | 896 | 0.1711 | 0.9376 | 0.9337 | 0.9356 | 0.9661 | |
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| 0.0274 | 9.0 | 1008 | 0.1621 | 0.9421 | 0.9440 | 0.9431 | 0.9735 | |
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| 0.0274 | 10.0 | 1120 | 0.1641 | 0.9489 | 0.9430 | 0.9459 | 0.9721 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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