---
library_name: transformers
license: mit
base_model: intfloat/multilingual-e5-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: fineweb-nemotron-edu-score
  results: []
language:
- uk
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# fineweb-nemotron-edu-score

This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an [transferred from English](https://huggingface.co/datasets/lapa-llm/classifier_source) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0489
- Precision: 0.9571
- Recall: 0.9605
- F1 Macro: 0.9588
- Accuracy: 0.9632

## Model description

This model measure educational value of the given text for humans, as labelled by Nemotron-340B model.

## Intended uses & limitations

Data filtering and evaluation of pretraining data at scale.

## Training and evaluation data

Take a look at https://github.com/lapa-llm/lapa-llm/blob/main/pretraining/quality-classifiers/fineweb_hf.py


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 0
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 1024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log        | 0      | 0    | 1.0257          | 0.3334    | 0.5    | 0.4001   | 0.6668   |
| 0.0562        | 1.3793 | 200  | 0.0520          | 0.9537    | 0.9549 | 0.9543   | 0.9593   |
| 0.0517        | 2.7586 | 400  | 0.0489          | 0.9571    | 0.9605 | 0.9588   | 0.9632   |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.6.0a0+ecf3bae40a.nv25.01
- Datasets 4.0.0
- Tokenizers 0.22.0