---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- HuggingFaceFW/fineweb-edu-llama3-annotations
language:
- en
---

[<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)
# distilbert-base-uncased: edu classifier

> [!IMPORTANT]  
> This is a (rare) encoder that supports flash attention 2!
> Use `attn_implementation="flash_attention_2"` when loading w/ [FA2 installed](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#installation-and-features)
> for faster inference.

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the HuggingFaceFW/fineweb-edu-llama3-annotations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2324
- Mse: 0.2324

## Usage 

Note this is for CPU, for GPU you will need to make some (small) changes.

```py
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("pszemraj/mpnet-base-edu-classifier")
model = AutoModelForSequenceClassification.from_pretrained("pszemraj/mpnet-base-edu-classifier")

text = "This is a test sentence."
inputs = tokenizer(text, return_tensors="pt", padding="longest", truncation=True)
outputs = model(**inputs)
logits = outputs.logits.squeeze(-1).float().detach().numpy()
score = logits.item()
result = {
    "text": text,
    "score": score,
    "int_score": int(round(max(0, min(score, 5)))),
}

print(result)
# {'text': 'This is a test sentence.', 'score': 0.3350256383419037, 'int_score': 0}
```

## Intended uses & limitations

Refer to the hf classifier's [model card](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier#limitations) for more details

## 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