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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: 10_epochs_camembert_jb |
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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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# 10_epochs_camembert_jb |
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This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1070 |
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- Overall Precision: 0.8279 |
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- Overall Recall: 0.8660 |
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- Overall F1: 0.8465 |
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- Overall Accuracy: 0.9803 |
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- Er F1: 0.8617 |
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- Oc F1: 0.8347 |
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- Umanprod F1: 0.7297 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Er F1 | Oc F1 | Umanprod F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:-----------:| |
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| 0.2805 | 1.0 | 613 | 0.0797 | 0.7802 | 0.7990 | 0.7895 | 0.9749 | 0.8187 | 0.7677 | 0.4231 | |
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| 0.072 | 2.0 | 1226 | 0.0790 | 0.8060 | 0.8392 | 0.8223 | 0.9773 | 0.8458 | 0.8050 | 0.5574 | |
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| 0.0511 | 3.0 | 1839 | 0.0807 | 0.8139 | 0.8623 | 0.8374 | 0.9789 | 0.8583 | 0.8200 | 0.6933 | |
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| 0.0354 | 4.0 | 2452 | 0.0808 | 0.8097 | 0.8574 | 0.8329 | 0.9793 | 0.8589 | 0.8115 | 0.6667 | |
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| 0.0198 | 5.0 | 3065 | 0.0940 | 0.7936 | 0.8591 | 0.8250 | 0.9781 | 0.8426 | 0.8124 | 0.6835 | |
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| 0.0165 | 6.0 | 3678 | 0.0988 | 0.8350 | 0.8542 | 0.8445 | 0.9802 | 0.8656 | 0.8297 | 0.6486 | |
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| 0.0126 | 7.0 | 4291 | 0.0990 | 0.8292 | 0.8692 | 0.8488 | 0.9805 | 0.8682 | 0.8340 | 0.6849 | |
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| 0.0103 | 8.0 | 4904 | 0.1042 | 0.8246 | 0.8666 | 0.8450 | 0.9803 | 0.8630 | 0.8331 | 0.6575 | |
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| 0.0076 | 9.0 | 5517 | 0.1066 | 0.8195 | 0.8687 | 0.8434 | 0.9801 | 0.8593 | 0.8305 | 0.7297 | |
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| 0.0066 | 10.0 | 6130 | 0.1070 | 0.8279 | 0.8660 | 0.8465 | 0.9803 | 0.8617 | 0.8347 | 0.7297 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cpu |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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