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README.md
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---
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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model-index:
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- name: distilbert-base-uncased-fineweb-edu-llama3-annotations-512-vN
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results: []
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/pszemraj/eduscore-regression/runs/k6z0kenz)
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# distilbert-base-uncased-fineweb-edu-llama3-annotations-512-vN
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2324
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- Mse: 0.2324
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 16
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- eval_batch_size: 16
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- seed: 90085
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-09
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.05
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- num_epochs: 1.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Mse |
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|:-------------:|:------:|:----:|:---------------:|:------:|
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| 0.5361 | 0.0288 | 100 | 0.4934 | 0.4934 |
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| 0.3483 | 0.0576 | 200 | 0.3525 | 0.3525 |
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| 0.3238 | 0.0865 | 300 | 0.2931 | 0.2931 |
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| 0.2734 | 0.1153 | 400 | 0.3130 | 0.3130 |
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| 0.2891 | 0.1441 | 500 | 0.3298 | 0.3298 |
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| 0.2807 | 0.1729 | 600 | 0.2659 | 0.2659 |
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| 0.2727 | 0.2018 | 700 | 0.2690 | 0.2690 |
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| 0.2701 | 0.2306 | 800 | 0.2555 | 0.2555 |
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| 0.2954 | 0.2594 | 900 | 0.2501 | 0.2501 |
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| 0.2618 | 0.2882 | 1000 | 0.2483 | 0.2483 |
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| 0.3081 | 0.3171 | 1100 | 0.2456 | 0.2456 |
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| 0.2544 | 0.3459 | 1200 | 0.2370 | 0.2370 |
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| 0.2593 | 0.3747 | 1300 | 0.2349 | 0.2349 |
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| 0.2361 | 0.4035 | 1400 | 0.2406 | 0.2406 |
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| 0.2536 | 0.4324 | 1500 | 0.2453 | 0.2453 |
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| 0.26 | 0.4612 | 1600 | 0.2568 | 0.2568 |
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| 0.2897 | 0.4900 | 1700 | 0.2568 | 0.2568 |
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| 0.2597 | 0.5188 | 1800 | 0.2359 | 0.2359 |
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| 0.2489 | 0.5477 | 1900 | 0.2413 | 0.2413 |
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| 0.2376 | 0.5765 | 2000 | 0.2416 | 0.2416 |
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| 0.2424 | 0.6053 | 2100 | 0.2418 | 0.2418 |
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| 0.2798 | 0.6341 | 2200 | 0.2462 | 0.2462 |
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| 0.2523 | 0.6630 | 2300 | 0.2322 | 0.2322 |
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| 0.286 | 0.6918 | 2400 | 0.2432 | 0.2432 |
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| 0.247 | 0.7206 | 2500 | 0.2383 | 0.2383 |
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| 0.2856 | 0.7494 | 2600 | 0.2375 | 0.2375 |
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| 0.2216 | 0.7783 | 2700 | 0.2383 | 0.2383 |
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| 0.255 | 0.8071 | 2800 | 0.2367 | 0.2367 |
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| 0.2406 | 0.8359 | 2900 | 0.2345 | 0.2345 |
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| 0.2388 | 0.8647 | 3000 | 0.2282 | 0.2282 |
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| 0.2571 | 0.8936 | 3100 | 0.2331 | 0.2331 |
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| 0.2672 | 0.9224 | 3200 | 0.2336 | 0.2336 |
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| 0.2375 | 0.9512 | 3300 | 0.2337 | 0.2337 |
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| 0.2423 | 0.9800 | 3400 | 0.2324 | 0.2324 |
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### Framework versions
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- Transformers 4.42.3
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- Pytorch 2.3.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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