---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-fineweb-edu-llama3-annotations-2048-vN
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<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)
# distilroberta-base-fineweb-edu-llama3-annotations-2048-vN

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2197
- Mse: 0.2197

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 90085
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-09
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Mse    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.5276        | 0.0288 | 100  | 0.5012          | 0.5012 |
| 0.3307        | 0.0576 | 200  | 0.3467          | 0.3467 |
| 0.2994        | 0.0865 | 300  | 0.2948          | 0.2948 |
| 0.2813        | 0.1153 | 400  | 0.2799          | 0.2799 |
| 0.2707        | 0.1441 | 500  | 0.3017          | 0.3017 |
| 0.2506        | 0.1729 | 600  | 0.2699          | 0.2699 |
| 0.2584        | 0.2018 | 700  | 0.2633          | 0.2633 |
| 0.2603        | 0.2306 | 800  | 0.2434          | 0.2434 |
| 0.2973        | 0.2594 | 900  | 0.2394          | 0.2394 |
| 0.2541        | 0.2882 | 1000 | 0.2356          | 0.2356 |
| 0.2837        | 0.3171 | 1100 | 0.2437          | 0.2437 |
| 0.242         | 0.3459 | 1200 | 0.2379          | 0.2379 |
| 0.2379        | 0.3747 | 1300 | 0.2270          | 0.2270 |
| 0.23          | 0.4035 | 1400 | 0.2357          | 0.2357 |
| 0.2345        | 0.4324 | 1500 | 0.2417          | 0.2417 |
| 0.2574        | 0.4612 | 1600 | 0.2556          | 0.2556 |
| 0.264         | 0.4900 | 1700 | 0.2452          | 0.2452 |
| 0.2596        | 0.5188 | 1800 | 0.2215          | 0.2215 |
| 0.244         | 0.5477 | 1900 | 0.2269          | 0.2269 |
| 0.2225        | 0.5765 | 2000 | 0.2342          | 0.2342 |
| 0.2475        | 0.6053 | 2100 | 0.2403          | 0.2403 |
| 0.253         | 0.6341 | 2200 | 0.2326          | 0.2326 |
| 0.2435        | 0.6630 | 2300 | 0.2161          | 0.2161 |
| 0.2865        | 0.6918 | 2400 | 0.2265          | 0.2265 |
| 0.2351        | 0.7206 | 2500 | 0.2343          | 0.2343 |
| 0.2582        | 0.7494 | 2600 | 0.2342          | 0.2342 |
| 0.2167        | 0.7783 | 2700 | 0.2337          | 0.2337 |
| 0.2495        | 0.8071 | 2800 | 0.2273          | 0.2273 |
| 0.2364        | 0.8359 | 2900 | 0.2298          | 0.2298 |
| 0.2236        | 0.8647 | 3000 | 0.2170          | 0.2170 |
| 0.231         | 0.8936 | 3100 | 0.2234          | 0.2234 |
| 0.2474        | 0.9224 | 3200 | 0.2227          | 0.2227 |
| 0.2333        | 0.9512 | 3300 | 0.2241          | 0.2241 |
| 0.2265        | 0.9800 | 3400 | 0.2197          | 0.2197 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1