---
base_model: microsoft/mpnet-base
tags:
- generated_from_trainer
model-index:
- name: mpnet-base-fineweb-edu-llama3-annotations-512-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/k2lc9nx3)
# mpnet-base-fineweb-edu-llama3-annotations-512-vN

This model is a fine-tuned version of [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the HuggingFaceFW/fineweb-edu-llama3-annotations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2105
- Mse: 0.2105

## 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: 8
- eval_batch_size: 8
- seed: 90085
- gradient_accumulation_steps: 16
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.5887        | 0.0288 | 100  | 0.6419          | 0.6419 |
| 0.371         | 0.0577 | 200  | 0.3439          | 0.3439 |
| 0.3607        | 0.0865 | 300  | 0.2844          | 0.2844 |
| 0.2576        | 0.1153 | 400  | 0.2589          | 0.2589 |
| 0.2822        | 0.1441 | 500  | 0.2707          | 0.2707 |
| 0.2908        | 0.1730 | 600  | 0.2382          | 0.2382 |
| 0.2258        | 0.2018 | 700  | 0.2405          | 0.2405 |
| 0.2604        | 0.2306 | 800  | 0.2318          | 0.2318 |
| 0.2961        | 0.2594 | 900  | 0.2186          | 0.2186 |
| 0.2453        | 0.2883 | 1000 | 0.2168          | 0.2168 |
| 0.278         | 0.3171 | 1100 | 0.2247          | 0.2247 |
| 0.2319        | 0.3459 | 1200 | 0.2142          | 0.2142 |
| 0.1983        | 0.3747 | 1300 | 0.2175          | 0.2175 |
| 0.2264        | 0.4036 | 1400 | 0.2306          | 0.2306 |
| 0.2175        | 0.4324 | 1500 | 0.2375          | 0.2375 |
| 0.2461        | 0.4612 | 1600 | 0.2493          | 0.2493 |
| 0.2419        | 0.4900 | 1700 | 0.2234          | 0.2234 |
| 0.2411        | 0.5189 | 1800 | 0.2137          | 0.2137 |
| 0.2473        | 0.5477 | 1900 | 0.2140          | 0.2140 |
| 0.237         | 0.5765 | 2000 | 0.2177          | 0.2177 |
| 0.1972        | 0.6053 | 2100 | 0.2186          | 0.2186 |
| 0.2556        | 0.6342 | 2200 | 0.2416          | 0.2416 |
| 0.2273        | 0.6630 | 2300 | 0.2197          | 0.2197 |
| 0.223         | 0.6918 | 2400 | 0.2253          | 0.2253 |
| 0.2028        | 0.7206 | 2500 | 0.2239          | 0.2239 |
| 0.2322        | 0.7495 | 2600 | 0.2180          | 0.2180 |
| 0.1933        | 0.7783 | 2700 | 0.2158          | 0.2158 |
| 0.2085        | 0.8071 | 2800 | 0.2298          | 0.2298 |
| 0.2038        | 0.8359 | 2900 | 0.2166          | 0.2166 |
| 0.2158        | 0.8648 | 3000 | 0.2084          | 0.2084 |
| 0.2197        | 0.8936 | 3100 | 0.2145          | 0.2145 |
| 0.2397        | 0.9224 | 3200 | 0.2163          | 0.2163 |
| 0.2307        | 0.9512 | 3300 | 0.2160          | 0.2160 |
| 0.2099        | 0.9801 | 3400 | 0.2101          | 0.2101 |


### Framework versions

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