Multiligual Arithmetic D64 L1 H2
This model is a fine-tuned version of []( on an unknown dataset.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 215 KB
- Versão
- vRevision 0fd2977
- Atualizado
- 21 de out. de 2026
- Publicador
- @CCBD-Interns
- Downloads
- 16
- Fonte
- Hugging Face
Descrição da fonte
multiligual-arithmetic-d64-l1-h2
This model is a fine-tuned version of on an unknown dataset.
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: 0.001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- 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: cosine
- lr_scheduler_warmup_steps: 0.05
- num_epochs: 10
Training results
Framework versions
- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Fontes
1 fonte
Fontes
1 fonteVerificado 21 de out.
Artefatos de modelo
1 artefato
Artefatos de modelo
1 artefatoTrechos de fonte
3 trechos
Trechos de fonte
3 trechos--- library_name: transformers tags: - generated_from_trainer model-index: - name: multiligual-arithmetic-d64-l1-h2 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. --> # multiligual-arithmetic-d64-l1-h2 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## 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: 0.001 - train_batch_size: 128 - eval_batch_size: 8 - seed: 42 - 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: cosine - lr_scheduler_warmup_steps: 0.05 - num_epochs: 10 ### Training results ### Framework versions - Transformers 5.12.0 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 16 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CCBD-Interns/multiligual-arithmetic-d64-l1-h2
Source context: 285 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo CCBD-Interns/multiligual-arithmetic-d64-l1-h2
