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mdeberta-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov

Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
Inference Endpoints
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/mdeberta-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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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: mdeberta-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov
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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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+ # mdeberta-ner-ghtk-hirach_NER-first_1000_data-3090-15Nov
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+
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+ This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0975
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+ - Accuracy: 0.9820
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+ - F1: 0.4359
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+ - Precision: 0.4857
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+ - Recall: 0.3953
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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: 2.5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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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: 40
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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 | F1 | Precision | Recall |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | No log | 1.0 | 250 | 0.0903 | 0.9825 | 0.0 | 0.0 | 0.0 |
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+ | 0.1391 | 2.0 | 500 | 0.0941 | 0.9825 | 0.0 | 0.0 | 0.0 |
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+ | 0.1391 | 3.0 | 750 | 0.0933 | 0.9825 | 0.0 | 0.0 | 0.0 |
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+ | 0.075 | 4.0 | 1000 | 0.0924 | 0.9825 | 0.0 | 0.0 | 0.0 |
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+ | 0.075 | 5.0 | 1250 | 0.0894 | 0.9825 | 0.0 | 0.0 | 0.0 |
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+ | 0.0634 | 6.0 | 1500 | 0.0870 | 0.9825 | 0.0851 | 0.5 | 0.0465 |
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+ | 0.0634 | 7.0 | 1750 | 0.0846 | 0.9820 | 0.0833 | 0.4 | 0.0465 |
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+ | 0.0508 | 8.0 | 2000 | 0.0799 | 0.9825 | 0.1224 | 0.5 | 0.0698 |
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+ | 0.0508 | 9.0 | 2250 | 0.0794 | 0.9829 | 0.125 | 0.6 | 0.0698 |
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+ | 0.0394 | 10.0 | 2500 | 0.0793 | 0.9800 | 0.0755 | 0.2 | 0.0465 |
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+ | 0.0394 | 11.0 | 2750 | 0.0801 | 0.9808 | 0.2034 | 0.375 | 0.1395 |
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+ | 0.0302 | 12.0 | 3000 | 0.0825 | 0.9812 | 0.2069 | 0.4 | 0.1395 |
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+ | 0.0302 | 13.0 | 3250 | 0.0763 | 0.9829 | 0.2759 | 0.5333 | 0.1860 |
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+ | 0.0232 | 14.0 | 3500 | 0.0755 | 0.9833 | 0.3692 | 0.5455 | 0.2791 |
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+ | 0.0232 | 15.0 | 3750 | 0.0799 | 0.9829 | 0.3226 | 0.5263 | 0.2326 |
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+ | 0.0176 | 16.0 | 4000 | 0.0785 | 0.9833 | 0.3692 | 0.5455 | 0.2791 |
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+ | 0.0176 | 17.0 | 4250 | 0.0776 | 0.9825 | 0.3768 | 0.5 | 0.3023 |
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+ | 0.0132 | 18.0 | 4500 | 0.0803 | 0.9833 | 0.3881 | 0.5417 | 0.3023 |
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+ | 0.0132 | 19.0 | 4750 | 0.0826 | 0.9812 | 0.3611 | 0.4483 | 0.3023 |
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+ | 0.0106 | 20.0 | 5000 | 0.0787 | 0.9825 | 0.4110 | 0.5 | 0.3488 |
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+ | 0.0106 | 21.0 | 5250 | 0.0879 | 0.9816 | 0.3478 | 0.4615 | 0.2791 |
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+ | 0.0085 | 22.0 | 5500 | 0.0848 | 0.9816 | 0.4156 | 0.4706 | 0.3721 |
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+ | 0.0085 | 23.0 | 5750 | 0.0818 | 0.9825 | 0.4267 | 0.5 | 0.3721 |
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+ | 0.0068 | 24.0 | 6000 | 0.0816 | 0.9833 | 0.4533 | 0.5312 | 0.3953 |
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+ | 0.0068 | 25.0 | 6250 | 0.0819 | 0.9825 | 0.4267 | 0.5 | 0.3721 |
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+ | 0.0056 | 26.0 | 6500 | 0.0848 | 0.9833 | 0.4533 | 0.5312 | 0.3953 |
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+ | 0.0056 | 27.0 | 6750 | 0.0872 | 0.9833 | 0.4533 | 0.5312 | 0.3953 |
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+ | 0.0049 | 28.0 | 7000 | 0.0844 | 0.9837 | 0.4595 | 0.5484 | 0.3953 |
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+ | 0.0049 | 29.0 | 7250 | 0.0881 | 0.9820 | 0.4211 | 0.4848 | 0.3721 |
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+ | 0.0042 | 30.0 | 7500 | 0.0925 | 0.9820 | 0.45 | 0.4865 | 0.4186 |
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+ | 0.0042 | 31.0 | 7750 | 0.0924 | 0.9825 | 0.4267 | 0.5 | 0.3721 |
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+ | 0.0038 | 32.0 | 8000 | 0.0938 | 0.9833 | 0.4675 | 0.5294 | 0.4186 |
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+ | 0.0038 | 33.0 | 8250 | 0.0939 | 0.9825 | 0.4416 | 0.5 | 0.3953 |
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+ | 0.0032 | 34.0 | 8500 | 0.0941 | 0.9833 | 0.4384 | 0.5333 | 0.3721 |
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+ | 0.0032 | 35.0 | 8750 | 0.0942 | 0.9833 | 0.4675 | 0.5294 | 0.4186 |
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+ | 0.0029 | 36.0 | 9000 | 0.0949 | 0.9820 | 0.4359 | 0.4857 | 0.3953 |
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+ | 0.0029 | 37.0 | 9250 | 0.0961 | 0.9820 | 0.4359 | 0.4857 | 0.3953 |
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+ | 0.0027 | 38.0 | 9500 | 0.0980 | 0.9820 | 0.4359 | 0.4857 | 0.3953 |
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+ | 0.0027 | 39.0 | 9750 | 0.0972 | 0.9820 | 0.4359 | 0.4857 | 0.3953 |
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+ | 0.0026 | 40.0 | 10000 | 0.0975 | 0.9820 | 0.4359 | 0.4857 | 0.3953 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.1.0
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+ - Tokenizers 0.19.1