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StephaneBah/
whisper-small-rad-fr2.0_lora2

Name: whisper-small-rad-fr2.0_lora2
Availability: InStock

transformers

safetensors

whisper

generated_from_trainer

StephaneBah/whisper-small-rad-fr1.1

finetune

apache-2.0

endpoints_compatible

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Whisper Small Fr - Radiologie2.0 Encoder-Layer[0:3]+ LoRa(QKVO; FFN)

This model is a fine-tuned version of StephaneBah/whisper-small-rad-fr1.1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8256
Wer: 34.6130

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: 8
eval_batch_size: 6
seed: 3407
optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	6.25	100	0.8140	34.4912
No log	12.5	200	0.8138	34.4912
No log	18.75	300	0.8165	34.4912
No log	25.0	400	0.8190	34.4302
0.0001	31.25	500	0.8202	34.5521
0.0001	37.5	600	0.8234	34.4302
0.0001	43.75	700	0.8248	34.4302
0.0001	50.0	800	0.8261	34.2474
0.0001	56.25	900	0.8266	34.6740
0.0	62.5	1000	0.8256	34.6130

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.2

StephaneBah/whisper-small-rad-fr2.0_lora2

Whisper Small Fr - Radiologie2.0 Encoder-Layer[0:3]+ LoRa(QKVO; FFN)

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

StephaneBah/
whisper-small-rad-fr2.0_lora2