---
license: apache-2.0
base_model: google/bigbird-roberta-base
tags:
- generated_from_trainer
model-index:
- name: bigbird-roberta-base-fineweb-edu-llama3-annotations-4096-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/04oc07hx)
# bigbird-roberta-base-fineweb-edu-llama3-annotations-4096-vN

This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the HuggingFaceFW/fineweb-edu-llama3-annotations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2176
- Mse: 0.2176

## 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: 4
- eval_batch_size: 4
- seed: 90085
- gradient_accumulation_steps: 32
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4763        | 0.0288 | 100  | 0.4468          | 0.4468 |
| 0.3078        | 0.0577 | 200  | 0.3130          | 0.3130 |
| 0.3088        | 0.0865 | 300  | 0.2695          | 0.2695 |
| 0.2379        | 0.1153 | 400  | 0.2618          | 0.2618 |
| 0.289         | 0.1441 | 500  | 0.2583          | 0.2583 |
| 0.3049        | 0.1730 | 600  | 0.2723          | 0.2723 |
| 0.2292        | 0.2018 | 700  | 0.2477          | 0.2477 |
| 0.2677        | 0.2306 | 800  | 0.2369          | 0.2369 |
| 0.3181        | 0.2594 | 900  | 0.2307          | 0.2307 |
| 0.2551        | 0.2883 | 1000 | 0.2411          | 0.2411 |
| 0.2743        | 0.3171 | 1100 | 0.2350          | 0.2350 |
| 0.2383        | 0.3459 | 1200 | 0.2424          | 0.2424 |
| 0.2191        | 0.3747 | 1300 | 0.2279          | 0.2279 |
| 0.2431        | 0.4036 | 1400 | 0.2232          | 0.2232 |
| 0.2161        | 0.4324 | 1500 | 0.2307          | 0.2307 |
| 0.2459        | 0.4612 | 1600 | 0.2246          | 0.2246 |
| 0.2403        | 0.4900 | 1700 | 0.2232          | 0.2232 |
| 0.251         | 0.5189 | 1800 | 0.2421          | 0.2421 |
| 0.2565        | 0.5477 | 1900 | 0.2207          | 0.2207 |
| 0.2274        | 0.5765 | 2000 | 0.2294          | 0.2294 |
| 0.2272        | 0.6053 | 2100 | 0.2192          | 0.2192 |
| 0.2668        | 0.6342 | 2200 | 0.2204          | 0.2204 |
| 0.2434        | 0.6630 | 2300 | 0.2196          | 0.2196 |
| 0.2464        | 0.6918 | 2400 | 0.2185          | 0.2185 |
| 0.2338        | 0.7206 | 2500 | 0.2166          | 0.2166 |
| 0.243         | 0.7495 | 2600 | 0.2165          | 0.2165 |
| 0.1891        | 0.7783 | 2700 | 0.2201          | 0.2201 |
| 0.2355        | 0.8071 | 2800 | 0.2167          | 0.2167 |
| 0.2231        | 0.8359 | 2900 | 0.2168          | 0.2168 |
| 0.2274        | 0.8648 | 3000 | 0.2243          | 0.2243 |
| 0.2287        | 0.8936 | 3100 | 0.2203          | 0.2203 |
| 0.261         | 0.9224 | 3200 | 0.2186          | 0.2186 |
| 0.2187        | 0.9512 | 3300 | 0.2176          | 0.2176 |
| 0.2069        | 0.9801 | 3400 | 0.2178          | 0.2178 |


### Framework versions

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