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copilot_relex_v1_with_context

Text Classification
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Safetensors
deberta-v2
Generated from Trainer
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  1. README.md +166 -0
  2. model.safetensors +1 -1
  3. runs/May02_18-45-02_aa2f90497f90/events.out.tfevents.1714675514.aa2f90497f90.270.0 +2 -2
README.md ADDED
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+ ---
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+ license: mit
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+ base_model: microsoft/deberta-v3-small
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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: copilot_relex_v1_with_context
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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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+ # copilot_relex_v1_with_context
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+
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+ This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0124
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+ - Accuracy: 0.0036
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+ - F1: 0.0059
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+ - Precision: 0.0030
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+ - Recall: 0.5938
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+ - Learning Rate: 0.0
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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: 32
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+ - eval_batch_size: 32
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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: 100
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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 | Rate |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
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+ | No log | 1.0 | 20 | 0.5600 | 0.1031 | 0.0104 | 0.0052 | 0.9375 | 0.0000 |
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+ | No log | 2.0 | 40 | 0.3965 | 0.0171 | 0.0101 | 0.0051 | 1.0 | 0.0000 |
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+ | No log | 3.0 | 60 | 0.2675 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 4.0 | 80 | 0.1762 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 5.0 | 100 | 0.1157 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 6.0 | 120 | 0.0800 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 7.0 | 140 | 0.0600 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 8.0 | 160 | 0.0485 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 9.0 | 180 | 0.0418 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 10.0 | 200 | 0.0375 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 11.0 | 220 | 0.0348 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 12.0 | 240 | 0.0330 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 13.0 | 260 | 0.0317 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 14.0 | 280 | 0.0307 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 15.0 | 300 | 0.0299 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 16.0 | 320 | 0.0293 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 17.0 | 340 | 0.0288 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 18.0 | 360 | 0.0279 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 19.0 | 380 | 0.0262 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 20.0 | 400 | 0.0268 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 21.0 | 420 | 0.0253 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 22.0 | 440 | 0.0245 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 23.0 | 460 | 0.0236 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 24.0 | 480 | 0.0234 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1128 | 25.0 | 500 | 0.0229 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1128 | 26.0 | 520 | 0.0221 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1128 | 27.0 | 540 | 0.0216 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1128 | 28.0 | 560 | 0.0219 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 29.0 | 580 | 0.0206 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 30.0 | 600 | 0.0200 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 31.0 | 620 | 0.0198 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 32.0 | 640 | 0.0195 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 33.0 | 660 | 0.0189 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 34.0 | 680 | 0.0192 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 35.0 | 700 | 0.0184 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 36.0 | 720 | 0.0180 | 0.0044 | 0.0087 | 0.0044 | 0.875 | 0.0000 |
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+ | 0.1128 | 37.0 | 740 | 0.0174 | 0.0044 | 0.0087 | 0.0044 | 0.875 | 0.0000 |
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+ | 0.1128 | 38.0 | 760 | 0.0172 | 0.0042 | 0.0084 | 0.0042 | 0.8438 | 0.0000 |
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+ | 0.1128 | 39.0 | 780 | 0.0167 | 0.0044 | 0.0087 | 0.0044 | 0.875 | 0.0000 |
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+ | 0.1128 | 40.0 | 800 | 0.0168 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1128 | 41.0 | 820 | 0.0168 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
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+ | 0.1128 | 42.0 | 840 | 0.0160 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
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+ | 0.1128 | 43.0 | 860 | 0.0156 | 0.0042 | 0.0084 | 0.0042 | 0.8438 | 0.0000 |
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+ | 0.1128 | 44.0 | 880 | 0.0159 | 0.0038 | 0.0072 | 0.0036 | 0.7188 | 0.0000 |
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+ | 0.1128 | 45.0 | 900 | 0.0153 | 0.0038 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
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+ | 0.1128 | 46.0 | 920 | 0.0155 | 0.0039 | 0.0078 | 0.0039 | 0.7812 | 0.0000 |
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+ | 0.1128 | 47.0 | 940 | 0.0149 | 0.0038 | 0.0072 | 0.0036 | 0.7188 | 0.0000 |
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+ | 0.1128 | 48.0 | 960 | 0.0148 | 0.0038 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
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+ | 0.1128 | 49.0 | 980 | 0.0149 | 0.0038 | 0.0072 | 0.0036 | 0.7188 | 0.0000 |
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+ | 0.0237 | 50.0 | 1000 | 0.0146 | 0.0036 | 0.0072 | 0.0036 | 0.7188 | 1e-05 |
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+ | 0.0237 | 51.0 | 1020 | 0.0143 | 0.0038 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
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+ | 0.0237 | 52.0 | 1040 | 0.0143 | 0.0038 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0237 | 53.0 | 1060 | 0.0145 | 0.0038 | 0.0072 | 0.0036 | 0.7188 | 0.0000 |
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+ | 0.0237 | 54.0 | 1080 | 0.0144 | 0.0036 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0237 | 55.0 | 1100 | 0.0140 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 9e-06 |
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+ | 0.0237 | 56.0 | 1120 | 0.0144 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0237 | 57.0 | 1140 | 0.0142 | 0.0038 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0237 | 58.0 | 1160 | 0.0140 | 0.0034 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0237 | 59.0 | 1180 | 0.0138 | 0.0038 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0237 | 60.0 | 1200 | 0.0137 | 0.0039 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0237 | 61.0 | 1220 | 0.0139 | 0.0038 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0237 | 62.0 | 1240 | 0.0139 | 0.0038 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0237 | 63.0 | 1260 | 0.0136 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0237 | 64.0 | 1280 | 0.0135 | 0.0039 | 0.0072 | 0.0036 | 0.7188 | 0.0000 |
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+ | 0.0237 | 65.0 | 1300 | 0.0133 | 0.0038 | 0.0066 | 0.0033 | 0.6562 | 7e-06 |
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+ | 0.0237 | 66.0 | 1320 | 0.0136 | 0.0038 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0237 | 67.0 | 1340 | 0.0137 | 0.0034 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0237 | 68.0 | 1360 | 0.0134 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0237 | 69.0 | 1380 | 0.0131 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0237 | 70.0 | 1400 | 0.0128 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 6e-06 |
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+ | 0.0237 | 71.0 | 1420 | 0.0129 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0237 | 72.0 | 1440 | 0.0131 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0237 | 73.0 | 1460 | 0.0129 | 0.0039 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0237 | 74.0 | 1480 | 0.0127 | 0.0036 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0179 | 75.0 | 1500 | 0.0129 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 5e-06 |
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+ | 0.0179 | 76.0 | 1520 | 0.0130 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0179 | 77.0 | 1540 | 0.0128 | 0.0033 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 78.0 | 1560 | 0.0125 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0179 | 79.0 | 1580 | 0.0127 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 80.0 | 1600 | 0.0126 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0179 | 81.0 | 1620 | 0.0124 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 82.0 | 1640 | 0.0124 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 83.0 | 1660 | 0.0126 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 84.0 | 1680 | 0.0127 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 85.0 | 1700 | 0.0124 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 3e-06 |
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+ | 0.0179 | 86.0 | 1720 | 0.0125 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 87.0 | 1740 | 0.0124 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 88.0 | 1760 | 0.0127 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 89.0 | 1780 | 0.0123 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 90.0 | 1800 | 0.0124 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 91.0 | 1820 | 0.0124 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 92.0 | 1840 | 0.0125 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 93.0 | 1860 | 0.0124 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 94.0 | 1880 | 0.0123 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 95.0 | 1900 | 0.0124 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 96.0 | 1920 | 0.0124 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 97.0 | 1940 | 0.0124 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 98.0 | 1960 | 0.0124 | 0.0034 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0179 | 99.0 | 1980 | 0.0124 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.0152 | 100.0 | 2000 | 0.0124 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.1
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.0
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+ - Tokenizers 0.19.1
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