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finerweb-binary-classifier-mdeberta-gemma3

Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
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  1. README.md +168 -0
  2. final/added_tokens.json +3 -0
  3. final/config.json +42 -0
  4. final/model.safetensors +3 -0
  5. final/special_tokens_map.json +15 -0
  6. final/spm.model +3 -0
  7. final/tokenizer.json +3 -0
  8. final/tokenizer_config.json +859 -0
  9. final/training_args.bin +3 -0
  10. model.safetensors +1 -1
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/mdeberta-v3-base
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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: gemma-fineweb-edu-scorer-mdeberta-binary-lr5e-05-20250411_140230
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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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+ # gemma-fineweb-edu-scorer-mdeberta-binary-lr5e-05-20250411_140230
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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: 0.1614
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1 Macro: 1.0
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+ - Accuracy: 1.0
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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: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 32
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+ - seed: 0
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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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+ - num_epochs: 20
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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 |
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+ |:-------------:|:-------:|:------:|:---------------:|:---------:|:------:|:--------:|:--------:|
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+ | No log | 0 | 0 | 0.2170 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1238 | 0.1954 | 1000 | 0.1458 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1143 | 0.3908 | 2000 | 0.1076 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1208 | 0.5862 | 3000 | 0.1133 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1031 | 0.7816 | 4000 | 0.1069 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1045 | 0.9769 | 5000 | 0.1047 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0974 | 1.1723 | 6000 | 0.1087 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1337 | 1.3677 | 7000 | 0.1209 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0864 | 1.5631 | 8000 | 0.1151 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.1049 | 1.7585 | 9000 | 0.1203 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0915 | 1.9539 | 10000 | 0.1255 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.079 | 2.1493 | 11000 | 0.1369 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0709 | 2.3447 | 12000 | 0.1167 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0823 | 2.5401 | 13000 | 0.1257 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0725 | 2.7354 | 14000 | 0.1114 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.066 | 2.9308 | 15000 | 0.1232 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0354 | 3.1262 | 16000 | 0.1321 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0486 | 3.3216 | 17000 | 0.1245 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0466 | 3.5170 | 18000 | 0.1329 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0523 | 3.7124 | 19000 | 0.1350 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0614 | 3.9078 | 20000 | 0.1373 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0349 | 4.1032 | 21000 | 0.1461 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0315 | 4.2986 | 22000 | 0.1406 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0337 | 4.4939 | 23000 | 0.1355 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0345 | 4.6893 | 24000 | 0.1300 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0324 | 4.8847 | 25000 | 0.1324 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0247 | 5.0801 | 26000 | 0.1347 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0306 | 5.2755 | 27000 | 0.1474 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0267 | 5.4709 | 28000 | 0.1394 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0204 | 5.6663 | 29000 | 0.1487 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0266 | 5.8617 | 30000 | 0.1454 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0188 | 6.0571 | 31000 | 0.1434 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0192 | 6.2524 | 32000 | 0.1458 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0246 | 6.4478 | 33000 | 0.1455 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0219 | 6.6432 | 34000 | 0.1440 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0231 | 6.8386 | 35000 | 0.1561 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0185 | 7.0340 | 36000 | 0.1504 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0163 | 7.2294 | 37000 | 0.1516 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0186 | 7.4248 | 38000 | 0.1451 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0226 | 8.0109 | 41000 | 0.1584 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0136 | 8.2063 | 42000 | 0.1589 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0186 | 8.4017 | 43000 | 0.1518 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0236 | 8.9879 | 46000 | 0.1409 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0114 | 13.2864 | 68000 | 0.1537 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0162 | 14.8496 | 76000 | 0.1513 | 1.0 | 1.0 | 1.0 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.49.0
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+ - Pytorch 2.6.0+cu124
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+ - Datasets 3.3.2
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+ - Tokenizers 0.21.1
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