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semantic_5_clusters_0_full_upsample1000
This model is a fine-tuned version of google/flan-t5-large 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: 0.0001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 256
- total_eval_batch_size: 16
- 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: 1.0
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
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Model tree for YWZBrandon/semantic_5_clusters_0_full_upsample1000
Base model
google/flan-t5-large
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("YWZBrandon/semantic_5_clusters_0_full_upsample1000") model = AutoModelForMultimodalLM.from_pretrained("YWZBrandon/semantic_5_clusters_0_full_upsample1000")