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README.md
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---
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license: apache-2.0
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base_model: distilroberta-base
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tags:
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- generated_from_trainer
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model-index:
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- name: distilroberta-base-fineweb-edu-llama3-annotations-2048-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/8e2uvp5t)
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# distilroberta-base-fineweb-edu-llama3-annotations-2048-vN
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2197
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- Mse: 0.2197
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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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| 0.5276 | 0.0288 | 100 | 0.5012 | 0.5012 |
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| 0.3307 | 0.0576 | 200 | 0.3467 | 0.3467 |
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| 0.2994 | 0.0865 | 300 | 0.2948 | 0.2948 |
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| 0.2813 | 0.1153 | 400 | 0.2799 | 0.2799 |
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| 0.2707 | 0.1441 | 500 | 0.3017 | 0.3017 |
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| 0.2506 | 0.1729 | 600 | 0.2699 | 0.2699 |
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| 0.2584 | 0.2018 | 700 | 0.2633 | 0.2633 |
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| 0.2603 | 0.2306 | 800 | 0.2434 | 0.2434 |
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| 0.2973 | 0.2594 | 900 | 0.2394 | 0.2394 |
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| 0.2541 | 0.2882 | 1000 | 0.2356 | 0.2356 |
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| 0.2837 | 0.3171 | 1100 | 0.2437 | 0.2437 |
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| 0.242 | 0.3459 | 1200 | 0.2379 | 0.2379 |
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| 0.2379 | 0.3747 | 1300 | 0.2270 | 0.2270 |
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| 0.23 | 0.4035 | 1400 | 0.2357 | 0.2357 |
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| 0.2345 | 0.4324 | 1500 | 0.2417 | 0.2417 |
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| 0.2574 | 0.4612 | 1600 | 0.2556 | 0.2556 |
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| 0.264 | 0.4900 | 1700 | 0.2452 | 0.2452 |
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| 0.2596 | 0.5188 | 1800 | 0.2215 | 0.2215 |
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| 0.244 | 0.5477 | 1900 | 0.2269 | 0.2269 |
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| 0.2225 | 0.5765 | 2000 | 0.2342 | 0.2342 |
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| 0.2475 | 0.6053 | 2100 | 0.2403 | 0.2403 |
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| 0.253 | 0.6341 | 2200 | 0.2326 | 0.2326 |
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| 0.2435 | 0.6630 | 2300 | 0.2161 | 0.2161 |
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| 0.2865 | 0.6918 | 2400 | 0.2265 | 0.2265 |
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| 0.2351 | 0.7206 | 2500 | 0.2343 | 0.2343 |
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| 0.2582 | 0.7494 | 2600 | 0.2342 | 0.2342 |
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| 0.2167 | 0.7783 | 2700 | 0.2337 | 0.2337 |
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| 0.2495 | 0.8071 | 2800 | 0.2273 | 0.2273 |
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| 0.2364 | 0.8359 | 2900 | 0.2298 | 0.2298 |
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| 0.2236 | 0.8647 | 3000 | 0.2170 | 0.2170 |
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| 0.231 | 0.8936 | 3100 | 0.2234 | 0.2234 |
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| 0.2474 | 0.9224 | 3200 | 0.2227 | 0.2227 |
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| 0.2333 | 0.9512 | 3300 | 0.2241 | 0.2241 |
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| 0.2265 | 0.9800 | 3400 | 0.2197 | 0.2197 |
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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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