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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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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: 0.2115
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- Precision: 0.9383
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- Recall: 0.9345
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- F1: 0.9364
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- Accuracy: 0.9565
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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 | 100 | 0.3620 | 0.8830 | 0.8673 | 0.8751 | 0.9167 |
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| No log | 2.0 | 200 | 0.2250 | 0.9176 | 0.9094 | 0.9134 | 0.9405 |
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| No log | 3.0 | 300 | 0.2174 | 0.9234 | 0.9167 | 0.9200 | 0.9433 |
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| No log | 4.0 | 400 | 0.2214 | 0.9213 | 0.9183 | 0.9198 | 0.9428 |
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| 0.3742 | 5.0 | 500 | 0.2099 | 0.9241 | 0.9264 | 0.9253 | 0.9474 |
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| 0.3742 | 6.0 | 600 | 0.1900 | 0.9412 | 0.9328 | 0.9370 | 0.9597 |
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| 0.3742 | 7.0 | 700 | 0.2174 | 0.9318 | 0.9280 | 0.9299 | 0.9515 |
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| 0.3742 | 8.0 | 800 | 0.1941 | 0.9360 | 0.9353 | 0.9357 | 0.9584 |
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| 0.3742 | 9.0 | 900 | 0.2157 | 0.9368 | 0.9361 | 0.9365 | 0.9565 |
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| 0.0315 | 10.0 | 1000 | 0.2115 | 0.9383 | 0.9345 | 0.9364 | 0.9565 |
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### Framework versions
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- Transformers 4.19.1
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- Pytorch 1.11.0+cu113
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- Datasets 2.2.1
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- Tokenizers 0.12.1
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