lapa-llm/fasttext-quality-score

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fasttext-quality-score

This model is a fine-tuned version of intfloat/multilingual-e5-base on an transferred from English. It achieves the following results on the evaluation set:

• —Loss: 0.1726
• —Precision: 0.7268
• —Recall: 0.6680
• —F1 Macro: 0.6791
• —Accuracy: 0.7465

Model description

This model measure the coherence of the given text, as defined by similarity to ELI5 texts from Reddit.

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/fasttext_classifier.py

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 8e-05
• —trainbatchsize: 32
• —evalbatchsize: 128
• —seed: 0
• —distributed_type: multi-GPU
• —num_devices: 8
• —totaltrainbatch_size: 256
• —totalevalbatch_size: 1024
• —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
• —lrschedulertype: cosine
• —lrschedulerwarmup_steps: 200
• —num_epochs: 20

Training results

Framework versions

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