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README.md ADDED
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+ ---
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+ base_model: bigcode/starencoder
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - accuracy
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+ model-index:
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+ - name: classifier-llama3-markdown-500k
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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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+ # classifier-llama3-markdown-500k
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+
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+ This model is a fine-tuned version of [bigcode/starencoder](https://huggingface.co/bigcode/starencoder) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3043
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+ - Precision: 0.6007
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+ - Recall: 0.5268
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+ - F1 Macro: 0.5255
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+ - Accuracy: 0.6694
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+ - F1 Binary Minimum3: 0.8552
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+ - F1 Binary Minimum2: 0.9316
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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: 0.0001
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+ - train_batch_size: 16
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+ - eval_batch_size: 256
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+ - seed: 0
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+ - distributed_type: multi-GPU
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+ - num_devices: 8
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+ - total_train_batch_size: 128
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+ - total_eval_batch_size: 2048
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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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+ - lr_scheduler_warmup_steps: 200
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | F1 Binary Minimum3 | F1 Binary Minimum2 |
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+ |:-------------:|:-------:|:------:|:---------------:|:---------:|:------:|:--------:|:--------:|:------------------:|:------------------:|
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+ | No log | 0 | 0 | 9.3055 | 0.0541 | 0.2 | 0.0852 | 0.2707 | 0 | 0 |
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+ | 0.3498 | 0.2946 | 1000 | 0.3644 | 0.5104 | 0.4920 | 0.4872 | 0.6121 | 0.8302 | 0.9179 |
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+ | 0.3395 | 0.5891 | 2000 | 0.3504 | 0.5128 | 0.4955 | 0.4867 | 0.6306 | 0.8445 | 0.9174 |
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+ | 0.3426 | 0.8837 | 3000 | 0.3387 | 0.5200 | 0.5029 | 0.5005 | 0.6349 | 0.8435 | 0.9229 |
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+ | 0.3533 | 1.1782 | 4000 | 0.3362 | 0.5195 | 0.5056 | 0.5032 | 0.6367 | 0.8441 | 0.9225 |
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+ | 0.3308 | 1.4728 | 5000 | 0.3306 | 0.5229 | 0.5065 | 0.5040 | 0.6456 | 0.8483 | 0.9242 |
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+ | 0.3453 | 1.7673 | 6000 | 0.3297 | 0.5248 | 0.5034 | 0.5020 | 0.6443 | 0.8495 | 0.9239 |
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+ | 0.3339 | 2.0619 | 7000 | 0.3258 | 0.5225 | 0.5106 | 0.5078 | 0.6494 | 0.8492 | 0.9247 |
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+ | 0.3442 | 2.3564 | 8000 | 0.3339 | 0.5441 | 0.5139 | 0.5114 | 0.6418 | 0.8423 | 0.9256 |
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+ | 0.323 | 2.6510 | 9000 | 0.3235 | 0.5249 | 0.5114 | 0.5090 | 0.6528 | 0.8503 | 0.9260 |
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+ | 0.3077 | 2.9455 | 10000 | 0.3230 | 0.5268 | 0.5096 | 0.5088 | 0.6495 | 0.8501 | 0.9260 |
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+ | 0.3215 | 3.2401 | 11000 | 0.3435 | 0.5266 | 0.5127 | 0.5092 | 0.6343 | 0.8378 | 0.9239 |
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+ | 0.3183 | 3.5346 | 12000 | 0.3287 | 0.5397 | 0.5205 | 0.5174 | 0.6495 | 0.8453 | 0.9268 |
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+ | 0.3205 | 3.8292 | 13000 | 0.3223 | 0.5430 | 0.5178 | 0.5155 | 0.6514 | 0.8481 | 0.9270 |
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+ | 0.3325 | 4.1237 | 14000 | 0.3200 | 0.5601 | 0.5166 | 0.5154 | 0.6533 | 0.8490 | 0.9273 |
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+ | 0.3057 | 4.4183 | 15000 | 0.3183 | 0.5282 | 0.5156 | 0.5141 | 0.6560 | 0.8507 | 0.9278 |
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+ | 0.2949 | 4.7128 | 16000 | 0.3176 | 0.5551 | 0.5184 | 0.5166 | 0.6577 | 0.8511 | 0.9276 |
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+ | 0.3045 | 5.0074 | 17000 | 0.3230 | 0.5263 | 0.5123 | 0.5086 | 0.6574 | 0.8517 | 0.9262 |
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+ | 0.3115 | 5.3019 | 18000 | 0.3200 | 0.5487 | 0.5209 | 0.5165 | 0.6569 | 0.8506 | 0.9262 |
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+ | 0.3065 | 5.5965 | 19000 | 0.3170 | 0.5813 | 0.5166 | 0.5157 | 0.6589 | 0.8522 | 0.9288 |
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+ | 0.3133 | 5.8910 | 20000 | 0.3201 | 0.5424 | 0.5243 | 0.5215 | 0.6580 | 0.8498 | 0.9285 |
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+ | 0.3099 | 6.1856 | 21000 | 0.3198 | 0.5389 | 0.5208 | 0.5184 | 0.6540 | 0.8487 | 0.9277 |
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+ | 0.3055 | 6.4801 | 22000 | 0.3190 | 0.5391 | 0.5247 | 0.5205 | 0.6585 | 0.8497 | 0.9286 |
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+ | 0.3176 | 6.7747 | 23000 | 0.3163 | 0.5574 | 0.5196 | 0.5185 | 0.6571 | 0.8511 | 0.9281 |
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+ | 0.3135 | 7.0692 | 24000 | 0.3149 | 0.5579 | 0.5192 | 0.5181 | 0.6590 | 0.8519 | 0.9287 |
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+ | 0.3061 | 7.3638 | 25000 | 0.3140 | 0.5540 | 0.5197 | 0.5185 | 0.6601 | 0.8520 | 0.9287 |
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+ | 0.3214 | 7.6583 | 26000 | 0.3149 | 0.5534 | 0.5228 | 0.5205 | 0.6597 | 0.8513 | 0.9287 |
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+ | 0.3024 | 7.9529 | 27000 | 0.3255 | 0.5368 | 0.5158 | 0.5149 | 0.6475 | 0.8486 | 0.9248 |
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+ | 0.2981 | 8.2474 | 28000 | 0.3234 | 0.5361 | 0.5195 | 0.5176 | 0.6510 | 0.8476 | 0.9271 |
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+ | 0.3057 | 8.5420 | 29000 | 0.3144 | 0.5445 | 0.5232 | 0.5212 | 0.6597 | 0.8508 | 0.9289 |
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+ | 0.3041 | 8.8365 | 30000 | 0.3149 | 0.5629 | 0.5201 | 0.5189 | 0.6572 | 0.8517 | 0.9283 |
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+ | 0.3082 | 9.1311 | 31000 | 0.3146 | 0.5477 | 0.5240 | 0.5221 | 0.6606 | 0.8518 | 0.9292 |
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+ | 0.3125 | 9.4256 | 32000 | 0.3120 | 0.5709 | 0.5190 | 0.5178 | 0.6625 | 0.8531 | 0.9296 |
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+ | 0.3072 | 9.7202 | 33000 | 0.3132 | 0.5721 | 0.5184 | 0.5186 | 0.6596 | 0.8530 | 0.9289 |
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+ | 0.3141 | 10.0147 | 34000 | 0.3121 | 0.5493 | 0.5242 | 0.5224 | 0.6628 | 0.8523 | 0.9294 |
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+ | 0.3022 | 10.3093 | 35000 | 0.3114 | 0.5696 | 0.5225 | 0.5206 | 0.6646 | 0.8530 | 0.9291 |
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+ | 0.3042 | 10.6038 | 36000 | 0.3132 | 0.5511 | 0.5264 | 0.5234 | 0.6629 | 0.8521 | 0.9293 |
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+ | 0.3063 | 10.8984 | 37000 | 0.3116 | 0.5688 | 0.5216 | 0.5189 | 0.6632 | 0.8531 | 0.9283 |
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+ | 0.307 | 11.1929 | 38000 | 0.3109 | 0.5645 | 0.5220 | 0.5210 | 0.6635 | 0.8530 | 0.9294 |
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+ | 0.314 | 11.4875 | 39000 | 0.3140 | 0.5618 | 0.5225 | 0.5189 | 0.6641 | 0.8530 | 0.9281 |
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+ | 0.3111 | 11.7820 | 40000 | 0.3143 | 0.5288 | 0.5181 | 0.5144 | 0.6627 | 0.8528 | 0.9278 |
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+ | 0.3279 | 12.0766 | 41000 | 0.3144 | 0.5602 | 0.5266 | 0.5242 | 0.6604 | 0.8506 | 0.9295 |
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+ | 0.3121 | 12.3711 | 42000 | 0.3097 | 0.5611 | 0.5221 | 0.5210 | 0.6655 | 0.8536 | 0.9303 |
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+ | 0.3055 | 12.6657 | 43000 | 0.3100 | 0.5571 | 0.5233 | 0.5220 | 0.6633 | 0.8535 | 0.9299 |
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+ | 0.3047 | 14.7275 | 50000 | 0.3086 | 0.5668 | 0.5244 | 0.5230 | 0.6663 | 0.8539 | 0.9309 |
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+ | 0.2932 | 15.0221 | 51000 | 0.3090 | 0.5612 | 0.5251 | 0.5237 | 0.6647 | 0.8537 | 0.9303 |
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+ | 0.302 | 15.6112 | 53000 | 0.3108 | 0.5529 | 0.5272 | 0.5244 | 0.6629 | 0.8521 | 0.9300 |
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+ | 0.3179 | 15.9057 | 54000 | 0.3081 | 0.5837 | 0.5225 | 0.5220 | 0.6645 | 0.8539 | 0.9302 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.4
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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+ "architectures": [
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+ "vocab_size": 49156
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+ }
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