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Final Model

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: avsolatorio/GIST-large-Embedding-v0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: chefdiff
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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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+ # chefdiff
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+
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+ This model is a fine-tuned version of [avsolatorio/GIST-large-Embedding-v0](https://huggingface.co/avsolatorio/GIST-large-Embedding-v0) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2966
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+ - F1: 0.6367
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+ - Roc Auc: 0.7929
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+ - Accuracy: 0.0909
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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: 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: 35
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.4056 | 1.0 | 50 | 0.3591 | 0.1786 | 0.5651 | 0.0182 |
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+ | 0.3103 | 2.0 | 100 | 0.3197 | 0.3214 | 0.6256 | 0.0909 |
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+ | 0.2522 | 3.0 | 150 | 0.2939 | 0.3551 | 0.6471 | 0.0727 |
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+ | 0.2039 | 4.0 | 200 | 0.2741 | 0.4813 | 0.7092 | 0.1273 |
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+ | 0.1632 | 5.0 | 250 | 0.2714 | 0.5493 | 0.7380 | 0.0909 |
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+ | 0.1308 | 6.0 | 300 | 0.2532 | 0.4740 | 0.6994 | 0.1636 |
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+ | 0.1056 | 7.0 | 350 | 0.2644 | 0.5641 | 0.7575 | 0.0909 |
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+ | 0.0837 | 8.0 | 400 | 0.2541 | 0.5684 | 0.7579 | 0.1091 |
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+ | 0.0679 | 9.0 | 450 | 0.2542 | 0.6112 | 0.7782 | 0.1273 |
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+ | 0.0562 | 10.0 | 500 | 0.2587 | 0.6342 | 0.7932 | 0.1455 |
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+ | 0.0476 | 11.0 | 550 | 0.2547 | 0.6399 | 0.7913 | 0.1455 |
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+ | 0.0421 | 12.0 | 600 | 0.2683 | 0.6429 | 0.7998 | 0.1273 |
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+ | 0.0369 | 13.0 | 650 | 0.2738 | 0.6146 | 0.7839 | 0.0909 |
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+ | 0.0335 | 14.0 | 700 | 0.2678 | 0.6387 | 0.7920 | 0.0909 |
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+ | 0.0298 | 15.0 | 750 | 0.2700 | 0.6123 | 0.7812 | 0.0909 |
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+ | 0.0275 | 16.0 | 800 | 0.2737 | 0.6152 | 0.7804 | 0.0909 |
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+ | 0.0255 | 17.0 | 850 | 0.2741 | 0.6339 | 0.7852 | 0.1091 |
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+ | 0.024 | 18.0 | 900 | 0.2778 | 0.6564 | 0.8052 | 0.1091 |
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+ | 0.0223 | 19.0 | 950 | 0.2815 | 0.6521 | 0.8019 | 0.1091 |
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+ | 0.0213 | 20.0 | 1000 | 0.2778 | 0.6296 | 0.7902 | 0.0727 |
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+ | 0.0201 | 21.0 | 1050 | 0.2861 | 0.6340 | 0.7900 | 0.0909 |
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+ | 0.0192 | 22.0 | 1100 | 0.2819 | 0.6413 | 0.7949 | 0.0909 |
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+ | 0.0185 | 23.0 | 1150 | 0.2913 | 0.6280 | 0.7888 | 0.0727 |
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+ | 0.0178 | 24.0 | 1200 | 0.2874 | 0.6347 | 0.7938 | 0.0727 |
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+ | 0.0172 | 25.0 | 1250 | 0.2890 | 0.6271 | 0.7879 | 0.0909 |
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+ | 0.0166 | 26.0 | 1300 | 0.2900 | 0.6306 | 0.7901 | 0.0727 |
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+ | 0.0161 | 27.0 | 1350 | 0.2944 | 0.6284 | 0.7896 | 0.0727 |
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+ | 0.0158 | 28.0 | 1400 | 0.2931 | 0.6407 | 0.7954 | 0.0909 |
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+ | 0.0154 | 29.0 | 1450 | 0.2942 | 0.6345 | 0.7937 | 0.0727 |
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+ | 0.015 | 30.0 | 1500 | 0.2960 | 0.6413 | 0.7942 | 0.0909 |
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+ | 0.0147 | 31.0 | 1550 | 0.2927 | 0.6477 | 0.7966 | 0.0909 |
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+ | 0.0147 | 32.0 | 1600 | 0.2952 | 0.6396 | 0.7943 | 0.0909 |
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+ | 0.0145 | 33.0 | 1650 | 0.2965 | 0.6314 | 0.7924 | 0.0727 |
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+ | 0.0144 | 34.0 | 1700 | 0.2971 | 0.6367 | 0.7929 | 0.0909 |
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+ | 0.0143 | 35.0 | 1750 | 0.2966 | 0.6367 | 0.7929 | 0.0909 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.1
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+ - Pytorch 2.1.2
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+ - Datasets 2.1.0
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+ - Tokenizers 0.15.2
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