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Distilbert 8 4 1.25 85 65 75

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: Loss: 0.3073 F1 Micro: 0.7857 F1 Macro: 0.7410 Precision Micro:...

TipoOtherModelo basedistilbert/distilbert-base-uncasedVersãoRevision 732e13fPublicador@DerivedFunction1Cnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
255 MB
Modelo base
distilbert/distilbert-base-uncased
Versão
vRevision 732e13f
Atualizado
12 de out. de 2026
Downloads
7
Fonte
Hugging Face

Descrição da fonte

distilbert-8-4-1.25-85-65-75

This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3073
  • F1 Micro: 0.7857
  • F1 Macro: 0.7410
  • Precision Micro: 0.8238
  • Recall Micro: 0.7510

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: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 6
  • total_train_batch_size: 24
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 3143
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training LossEpochStepValidation LossF1 MicroF1 MacroPrecision MicroRecall Micro
1.87021.0104790.31110.76480.71280.81620.7195
1.85512.0209580.30900.78620.74770.81780.7569
1.84603.0314370.30820.78860.75120.82660.7539

Framework versions

  • Transformers 5.10.2
  • Pytorch 2.11.0+cu128
  • Datasets 4.8.5
  • Tokenizers 0.22.2

Fontes

1 fonte
Hugging FacePrimárioVersão 732e13fd529957fe764461526333a6562b617d73
huggingface.co/emotions-entailment/distilbert-8-4-1.25-85-65-75/tree/732e13fd529957fe764461526333a6562b617d73

Verificado 21 de set.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 255 MB · SHA-256 e4f0b0492b87…53d0 · Hugging Face

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Trechos de fonte

3 trechos

--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-8-4-1.25-85-65-75 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. --> # distilbert-8-4-1.25-85-65-75 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3073 - F1 Micro: 0.7857 - F1 Macro: 0.7410 - Precision Micro: 0.8238 - Recall Micro: 0.7510 ## 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 6 - total_train_batch_size: 24 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 3143 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Precision Micro | Recall Micro | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:---------:|:----------------:|:-------------:| | 1.8702 | 1.0 | 10479 | 0.3111 | 0.7648 | 0.7128 | 0.8162 | 0.7195 | | 1.8551 | 2.0 | 20958 | 0.3090 | 0.7862 | 0.7477 | 0.8178 | 0.7569 | | 1.8460 | 3.0 | 31437 | 0.3082 | 0.7886 | 0.7512 | 0.8266 | 0.7539 | ### Framework versions - Transformers 5.10.2 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.22.2

Source context: 7 downloads · 0 likes · Pipeline text-classification · Library transformers · Repo emotions-entailment/distilbert-8-4-1.25-85-65-75

Source context: 4 downloads · 0 likes · Pipeline text-classification · Library transformers · Repo emotions-entailment/distilbert-8-4-1.25-85-65-75