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RKessler
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VogagenRelation

Text Classification
Transformers
PyTorch
camembert
Generated from Trainer
Inference Endpoints
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End of training

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  1. README.md +35 -23
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -5,6 +5,9 @@ tags:
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
 
 
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  model-index:
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  - name: VogagenRelation
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  results: []
@@ -17,8 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4009
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- - Accuracy: 0.8885
 
 
 
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 0.21 | 100 | 0.6667 | 0.6006 |
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- | No log | 0.42 | 200 | 0.6039 | 0.7137 |
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- | No log | 0.62 | 300 | 0.6321 | 0.6677 |
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- | No log | 0.83 | 400 | 0.5410 | 0.7715 |
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- | 0.6555 | 1.04 | 500 | 0.5710 | 0.7317 |
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- | 0.6555 | 1.25 | 600 | 0.5570 | 0.7621 |
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- | 0.6555 | 1.46 | 700 | 0.5420 | 0.7769 |
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- | 0.6555 | 1.66 | 800 | 0.5119 | 0.8011 |
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- | 0.6555 | 1.87 | 900 | 0.3608 | 0.8674 |
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- | 0.4846 | 2.08 | 1000 | 0.4312 | 0.8565 |
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- | 0.4846 | 2.29 | 1100 | 0.5109 | 0.8409 |
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- | 0.4846 | 2.49 | 1200 | 0.3783 | 0.8768 |
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- | 0.4846 | 2.7 | 1300 | 0.5244 | 0.8354 |
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- | 0.4846 | 2.91 | 1400 | 0.3986 | 0.8713 |
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- | 0.3439 | 3.12 | 1500 | 0.4613 | 0.8736 |
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- | 0.3439 | 3.33 | 1600 | 0.3658 | 0.8846 |
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- | 0.3439 | 3.53 | 1700 | 0.3890 | 0.8830 |
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- | 0.3439 | 3.74 | 1800 | 0.4028 | 0.8877 |
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- | 0.3439 | 3.95 | 1900 | 0.4009 | 0.8885 |
 
 
 
 
 
 
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  ### Framework versions
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
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+ - precision
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+ - recall
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+ - f1
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  model-index:
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  - name: VogagenRelation
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  results: []
 
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  This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4869
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+ - Accuracy: 0.9016
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+ - Precision: 0.8671
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+ - Recall: 0.9484
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+ - F1: 0.9060
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | No log | 0.21 | 100 | 0.6272 | 0.6729 | 0.6415 | 0.7828 | 0.7051 |
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+ | No log | 0.42 | 200 | 0.4933 | 0.7799 | 0.7406 | 0.8609 | 0.7962 |
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+ | No log | 0.62 | 300 | 0.4114 | 0.8431 | 0.8087 | 0.8984 | 0.8512 |
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+ | No log | 0.83 | 400 | 0.4483 | 0.8384 | 0.8054 | 0.8922 | 0.8466 |
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+ | 0.5445 | 1.04 | 500 | 0.4149 | 0.8525 | 0.7971 | 0.9453 | 0.8649 |
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+ | 0.5445 | 1.25 | 600 | 0.4221 | 0.8532 | 0.8038 | 0.9344 | 0.8642 |
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+ | 0.5445 | 1.46 | 700 | 0.4022 | 0.8712 | 0.8728 | 0.8688 | 0.8708 |
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+ | 0.5445 | 1.66 | 800 | 0.4083 | 0.8509 | 0.8013 | 0.9328 | 0.8621 |
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+ | 0.5445 | 1.87 | 900 | 0.4272 | 0.8704 | 0.8455 | 0.9062 | 0.8748 |
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+ | 0.3857 | 2.08 | 1000 | 0.3800 | 0.8759 | 0.8501 | 0.9125 | 0.8802 |
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+ | 0.3857 | 2.29 | 1100 | 0.4684 | 0.8673 | 0.8357 | 0.9141 | 0.8731 |
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+ | 0.3857 | 2.49 | 1200 | 0.4754 | 0.8634 | 0.8207 | 0.9297 | 0.8718 |
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+ | 0.3857 | 2.7 | 1300 | 0.4392 | 0.8681 | 0.8294 | 0.9266 | 0.8753 |
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+ | 0.3857 | 2.91 | 1400 | 0.5272 | 0.8470 | 0.7803 | 0.9656 | 0.8631 |
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+ | 0.2687 | 3.12 | 1500 | 0.3529 | 0.9016 | 0.8693 | 0.9453 | 0.9057 |
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+ | 0.2687 | 3.33 | 1600 | 0.3857 | 0.8899 | 0.8499 | 0.9469 | 0.8958 |
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+ | 0.2687 | 3.53 | 1700 | 0.3852 | 0.9016 | 0.8836 | 0.925 | 0.9038 |
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+ | 0.2687 | 3.74 | 1800 | 0.4860 | 0.8829 | 0.8365 | 0.9516 | 0.8904 |
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+ | 0.2687 | 3.95 | 1900 | 0.4014 | 0.9001 | 0.8657 | 0.9469 | 0.9045 |
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+ | 0.1785 | 4.16 | 2000 | 0.4295 | 0.8993 | 0.8655 | 0.9453 | 0.9037 |
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+ | 0.1785 | 4.37 | 2100 | 0.4592 | 0.8977 | 0.8550 | 0.9578 | 0.9035 |
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+ | 0.1785 | 4.57 | 2200 | 0.4392 | 0.9055 | 0.8844 | 0.9328 | 0.9080 |
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+ | 0.1785 | 4.78 | 2300 | 0.4659 | 0.9024 | 0.8759 | 0.9375 | 0.9057 |
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+ | 0.1785 | 4.99 | 2400 | 0.4059 | 0.9110 | 0.9021 | 0.9219 | 0.9119 |
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+ | 0.1098 | 5.2 | 2500 | 0.4869 | 0.9016 | 0.8671 | 0.9484 | 0.9060 |
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  ### Framework versions
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