You can deploy this model and start earning money today!

henilshah/
banking77-MiniLM-L12-H384

Name: banking77-MiniLM-L12-H384
Availability: InStock

generated_from_trainer

microsoft/MiniLM-L12-H384-uncased

finetune

mit

text-embeddings-inference

endpoints_compatible

Views

No views yet

banking77-MiniLM-L12-H384

This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.3196
Accuracy: 0.3696
F1 Macro: 0.2759

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: 3e-05
train_batch_size: 32
eval_batch_size: 64
seed: 42
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: 93
num_epochs: 3
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
3.7805	1.0	313	3.7746	0.2120	0.1324
3.4233	2.0	626	3.4277	0.3157	0.2194
3.3011	3.0	939	3.3196	0.3696	0.2759

Framework versions

Transformers 5.6.0
Pytorch 2.11.0+cu130
Datasets 4.8.4
Tokenizers 0.22.2

henilshah/banking77-MiniLM-L12-H384

banking77-MiniLM-L12-H384

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

henilshah/
banking77-MiniLM-L12-H384