README.md

metadata

library_name: transformers
license: mit
base_model: agentlans/multilingual-e5-small-aligned-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
language:
  - ar
  - zh
  - cs
  - da
  - nl
  - fr
  - de
  - el
  - hu
  - id
  - it
  - ja
  - fa
  - pl
  - pt
  - ru
  - es
  - sv
  - tr
  - vi
datasets:
  - agentlans/fineweb2hq-vs-c4
pipeline_tag: text-classification

agentlans/multilingual-e5-small-fineweb2hq-vs-c4-classifier

Note: This model is provided for reference and reproducibility, not for standalone use.

This model is a fine-tuned version of agentlans/multilingual-e5-small-aligned-v2 on the agentlans/fineweb2hq-vs-c4 dataset.

The aim is to classify text as higher quality (FineWeb 2 HQ) or lower quality (C4) for AI training.

On the validation set:

• Loss: 0.1983
• Accuracy: 0.9515
• Combined Score: 1.3494
• Num Input Tokens Seen: 122880000

Example

from transformers import pipeline

classifier = pipeline("text-classification", model="agentlans/multilingual-e5-small-fineweb2hq-vs-c4-classifier")
classifier("Your text here.")

Limitations

• Not trained on English data
• Tends to be overly permissive, labelling most texts outside training data as high quality
• May be biased against some text types

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use 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: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Combined Score	Input Tokens Seen
0.1387	1.0	40000	0.1983	0.9515	1.3494	40960000
0.0682	2.0	80000	0.2264	0.9528	1.3270	81920000
0.0424	3.0	120000	0.2598	0.9552	1.2845	122880000

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0