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yanekyuk
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camembert-keyword-discriminator

Token Classification
Transformers
PyTorch
camembert
Generated from Trainer
Inference Endpoints
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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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+ metrics:
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+ - precision
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+ - recall
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: camembert-keyword-discriminator
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+ results: []
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+ ---
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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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+
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+ # camembert-keyword-discriminator
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+
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+ This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2180
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+ - Precision: 0.6646
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+ - Recall: 0.7047
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+ - Accuracy: 0.9344
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+ - F1: 0.6841
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+ - Ent/precision: 0.7185
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+ - Ent/accuracy: 0.8157
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+ - Ent/f1: 0.7640
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+ - Con/precision: 0.5324
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+ - Con/accuracy: 0.4860
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+ - Con/f1: 0.5082
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
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+ | 0.2016 | 1.0 | 1875 | 0.1910 | 0.5947 | 0.7125 | 0.9243 | 0.6483 | 0.6372 | 0.8809 | 0.7395 | 0.4560 | 0.3806 | 0.4149 |
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+ | 0.1454 | 2.0 | 3750 | 0.1632 | 0.6381 | 0.7056 | 0.9324 | 0.6701 | 0.6887 | 0.8291 | 0.7524 | 0.5064 | 0.4621 | 0.4833 |
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+ | 0.1211 | 3.0 | 5625 | 0.1702 | 0.6703 | 0.6678 | 0.9343 | 0.6690 | 0.7120 | 0.7988 | 0.7529 | 0.5471 | 0.4094 | 0.4684 |
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+ | 0.1021 | 4.0 | 7500 | 0.1745 | 0.6777 | 0.6708 | 0.9351 | 0.6742 | 0.7206 | 0.7956 | 0.7562 | 0.5557 | 0.4248 | 0.4815 |
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+ | 0.0886 | 5.0 | 9375 | 0.1913 | 0.6540 | 0.7184 | 0.9340 | 0.6847 | 0.7022 | 0.8396 | 0.7648 | 0.5288 | 0.4795 | 0.5030 |
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+ | 0.0781 | 6.0 | 11250 | 0.2021 | 0.6605 | 0.7132 | 0.9344 | 0.6858 | 0.7139 | 0.8258 | 0.7658 | 0.5293 | 0.4913 | 0.5096 |
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+ | 0.0686 | 7.0 | 13125 | 0.2127 | 0.6539 | 0.7132 | 0.9337 | 0.6822 | 0.7170 | 0.8172 | 0.7638 | 0.5112 | 0.5083 | 0.5098 |
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+ | 0.0667 | 8.0 | 15000 | 0.2180 | 0.6646 | 0.7047 | 0.9344 | 0.6841 | 0.7185 | 0.8157 | 0.7640 | 0.5324 | 0.4860 | 0.5082 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.19.2
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.2.2
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+ - Tokenizers 0.12.1