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judge_answer___33_deberta_base_enwiki-answerability-2411

Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/deberta-v3-base
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+ tags:
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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: judge_answer___33_deberta_base_enwiki-answerability-2411
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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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+ # judge_answer___33_deberta_base_enwiki-answerability-2411
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2045
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+ - Accuracy: 0.9442
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+ - Precision: 0.9592
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+ - Recall: 0.9541
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+ - F1: 0.9566
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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: 1e-05
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+ - train_batch_size: 2
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: 16
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 500
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+ - num_epochs: 1
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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 | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 4.2448 | 0.0267 | 500 | 0.3176 | 0.8796 | 0.9528 | 0.8556 | 0.9016 |
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+ | 2.4191 | 0.0533 | 1000 | 0.2767 | 0.9138 | 0.9271 | 0.9403 | 0.9336 |
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+ | 1.9636 | 0.08 | 1500 | 0.2391 | 0.9142 | 0.9607 | 0.9039 | 0.9314 |
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+ | 1.7834 | 0.1067 | 2000 | 0.2148 | 0.9254 | 0.9643 | 0.9183 | 0.9407 |
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+ | 1.675 | 0.1333 | 2500 | 0.2138 | 0.9292 | 0.9560 | 0.9332 | 0.9444 |
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+ | 1.7663 | 0.16 | 3000 | 0.2685 | 0.9177 | 0.9747 | 0.8956 | 0.9335 |
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+ | 1.7593 | 0.1867 | 3500 | 0.2410 | 0.9254 | 0.9678 | 0.9147 | 0.9405 |
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+ | 1.7727 | 0.2133 | 4000 | 0.1939 | 0.9354 | 0.9592 | 0.9397 | 0.9494 |
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+ | 1.6498 | 0.24 | 4500 | 0.2019 | 0.9358 | 0.9564 | 0.9433 | 0.9498 |
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+ | 1.5754 | 0.2667 | 5000 | 0.2334 | 0.9377 | 0.9577 | 0.9451 | 0.9514 |
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+ | 1.6312 | 0.2933 | 5500 | 0.2120 | 0.935 | 0.9671 | 0.9308 | 0.9486 |
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+ | 1.5515 | 0.32 | 6000 | 0.2342 | 0.9354 | 0.9637 | 0.9350 | 0.9491 |
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+ | 1.6374 | 0.3467 | 6500 | 0.2143 | 0.9385 | 0.9555 | 0.9487 | 0.9521 |
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+ | 1.6782 | 0.3733 | 7000 | 0.1865 | 0.9373 | 0.9599 | 0.9421 | 0.9509 |
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+ | 1.614 | 0.4 | 7500 | 0.2039 | 0.9404 | 0.9562 | 0.9511 | 0.9536 |
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+ | 1.5568 | 0.4267 | 8000 | 0.1862 | 0.9423 | 0.9641 | 0.9457 | 0.9548 |
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+ | 1.5774 | 0.4533 | 8500 | 0.1818 | 0.94 | 0.9634 | 0.9427 | 0.9530 |
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+ | 1.5722 | 0.48 | 9000 | 0.2388 | 0.9396 | 0.9628 | 0.9427 | 0.9527 |
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+ | 1.5544 | 0.5067 | 9500 | 0.2009 | 0.9408 | 0.9635 | 0.9439 | 0.9536 |
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+ | 1.5426 | 0.5333 | 10000 | 0.2398 | 0.9385 | 0.9662 | 0.9374 | 0.9515 |
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+ | 1.5144 | 0.56 | 10500 | 0.2223 | 0.9381 | 0.9662 | 0.9368 | 0.9512 |
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+ | 1.508 | 0.5867 | 11000 | 0.2135 | 0.9446 | 0.9517 | 0.9630 | 0.9573 |
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+ | 1.5881 | 0.6133 | 11500 | 0.1886 | 0.9404 | 0.9546 | 0.9529 | 0.9537 |
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+ | 1.4951 | 0.64 | 12000 | 0.2053 | 0.9442 | 0.9671 | 0.9457 | 0.9563 |
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+ | 1.583 | 0.6667 | 12500 | 0.2088 | 0.9412 | 0.9663 | 0.9415 | 0.9538 |
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+ | 1.5312 | 0.6933 | 13000 | 0.2041 | 0.9373 | 0.9719 | 0.9296 | 0.9503 |
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+ | 1.5474 | 0.72 | 13500 | 0.1907 | 0.9412 | 0.9663 | 0.9415 | 0.9538 |
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+ | 1.4928 | 0.7467 | 14000 | 0.1998 | 0.9438 | 0.9631 | 0.9493 | 0.9561 |
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+ | 1.5224 | 0.7733 | 14500 | 0.1940 | 0.9423 | 0.9591 | 0.9511 | 0.9551 |
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+ | 1.5267 | 0.8 | 15000 | 0.2095 | 0.9442 | 0.9665 | 0.9463 | 0.9563 |
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+ | 1.6073 | 0.8267 | 15500 | 0.1905 | 0.945 | 0.9620 | 0.9523 | 0.9571 |
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+ | 1.4924 | 0.8533 | 16000 | 0.2118 | 0.9462 | 0.9666 | 0.9493 | 0.9579 |
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+ | 1.543 | 0.88 | 16500 | 0.2074 | 0.9442 | 0.9603 | 0.9529 | 0.9566 |
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+ | 1.6774 | 0.9067 | 17000 | 0.2044 | 0.9446 | 0.9631 | 0.9505 | 0.9568 |
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+ | 1.5077 | 0.9333 | 17500 | 0.2007 | 0.945 | 0.9626 | 0.9517 | 0.9571 |
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+ | 1.4738 | 0.96 | 18000 | 0.2018 | 0.9442 | 0.9603 | 0.9529 | 0.9566 |
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+ | 1.4543 | 0.9867 | 18500 | 0.2045 | 0.9442 | 0.9592 | 0.9541 | 0.9566 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.0
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.2
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+ - Tokenizers 0.20.1
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