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Exp23 Directfit Unfrozen

This model is a fine-tuned version of cyttic/exp22-exp2warm-directfit-frozen on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.4588 Cer: 0.0245 Wer: 0.0647

Quellehuggingface.co/cyttic/exp23-directfit-unfrozen/tree/57b28f8ac5e8e2d9ff910b7126efbda032fa2a8d
ArtCheckpointBasismodellcyttic/exp22-exp2warm-directfit-frozenVersionRevision 57b28f8DBewertung

Modellquelle

Art
Checkpoint
Dateigröße
1,12 GB
Basismodell
cyttic/exp22-exp2warm-directfit-frozen
Version
vRevision 57b28f8
Aktualisiert
1. Okt. 2026
Downloads
173
Quelle
Hugging Face

Quellenbeschreibung

exp23-directfit-unfrozen

This model is a fine-tuned version of cyttic/exp22-exp2warm-directfit-frozen on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4588
  • Cer: 0.0245
  • Wer: 0.0647

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: 16
  • eval_batch_size: 16
  • 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: 0.1
  • num_epochs: 1

Training results

Training LossEpochStepValidation LossCerWer
1.25770.042425001.33780.14160.2986
1.04130.084850001.24410.11500.2561
0.84360.127275001.08540.09170.2106
0.77620.1696100000.93140.07350.1700
0.70960.2120125000.89540.06270.1534
0.62050.2544150000.79070.05500.1378
0.58730.2968175000.74080.04860.1215
0.55040.3392200000.68940.04270.1112
0.48260.3816225000.65370.04060.1042
0.52240.4240250000.62450.03800.0965
0.44300.4664275000.58660.03660.0933
0.42190.5088300000.57240.03400.0873
0.35570.5512325000.55860.03180.0849
0.38660.5936350000.53990.03280.0845
0.35860.6360375000.52670.03050.0793
0.40090.6784400000.50820.03010.0769
0.31400.7208425000.50110.02890.0740
0.34350.7632450000.48740.02760.0725
0.31410.8056475000.48070.02670.0691
0.32000.8480500000.47380.02670.0687
0.32010.8904525000.46820.02500.0664
0.33140.9328550000.46330.02490.0652
0.34270.9752575000.46040.02440.0648
0.30201.0589620.45880.02450.0647

Framework versions

  • Transformers 5.12.1
  • Pytorch 2.11.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2

Quellen

1 Quelle
Hugging FacePrimärVersion 57b28f8ac5e8e2d9ff910b7126efbda032fa2a8d
huggingface.co/cyttic/exp23-directfit-unfrozen/tree/57b28f8ac5e8e2d9ff910b7126efbda032fa2a8d

Verifiziert 1. Okt.

Modellartefakte

2 Artefakte

last-checkpoint/model.safetensors

safetensors · 1,12 GB · SHA-256 f4206fdf41f5…d1c9 · Hugging Face

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model.safetensors

safetensors · 1,12 GB · SHA-256 f4206fdf41f5…d1c9 · Hugging Face

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Quellenauszüge

3 Auszüge
Readme11. Julihuggingface.co/cyttic/exp23-directfit-unfrozen

--- library_name: transformers base_model: cyttic/exp22-exp2warm-directfit-frozen tags: - generated_from_trainer metrics: - wer model-index: - name: exp23-directfit-unfrozen 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. --> # exp23-directfit-unfrozen This model is a fine-tuned version of [cyttic/exp22-exp2warm-directfit-frozen](https://huggingface.co/cyttic/exp22-exp2warm-directfit-frozen) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4588 - Cer: 0.0245 - Wer: 0.0647 ## 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: 16 - eval_batch_size: 16 - 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: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:|:------:| | 1.2577 | 0.0424 | 2500 | 1.3378 | 0.1416 | 0.2986 | | 1.0413 | 0.0848 | 5000 | 1.2441 | 0.1150 | 0.2561 | | 0.8436 | 0.1272 | 7500 | 1.0854 | 0.0917 | 0.2106 | | 0.7762 | 0.1696 | 10000 | 0.9314 | 0.0735 | 0.1700 | | 0.7096 | 0.2120 | 12500 | 0.8954 | 0.0627 | 0.1534 | | 0.6205 | 0.2544 | 15000 | 0.7907 | 0.0550 | 0.1378 | | 0.5873 | 0.2968 | 17500 | 0.7408 | 0.0486 | 0.1215 | | 0.5504 | 0.3392 | 20000 | 0.6894 | 0.0427 | 0.1112 | | 0.4826 | 0.3816 | 22500 | 0.6537 | 0.0406 | 0.1042 | | 0.5224 | 0.4240 | 25000 | 0.6245 | 0.0380 | 0.0965 | | 0.4430 | 0.4664 | 27500 | 0.5866 | 0.0366 | 0.0933 | | 0.4219 | 0.5088 | 30000 | 0.5724 | 0.0340 | 0.0873 | | 0.3557 | 0.5512 | 32500 | 0.5586 | 0.0318 | 0.0849 | | 0.3866 | 0.5936 | 35000 | 0.5399 | 0.0328 | 0.0845 | | 0.3586 | 0.6360 | 37500 | 0.5267 | 0.0305 | 0.0793 | | 0.4009 | 0.6784 | 40000 | 0.5082 | 0.0301 | 0.0769 | | 0.3140 | 0.7208 | 42500 | 0.5011 | 0.0289 | 0.0740 | | 0.3435 | 0.7632 | 45000 | 0.4874 | 0.0276 | 0.0725 | | 0.314...

Note1. Okt.huggingface.co/cyttic/exp23-directfit-unfrozen

Source context: 173 downloads · 1 likes · Pipeline image-text-to-text · Library transformers · Repo cyttic/exp23-directfit-unfrozen

Note16. Julihuggingface.co/cyttic/exp23-directfit-unfrozen

Source context: 467 downloads · 1 likes · Pipeline image-text-to-text · Library transformers · Repo cyttic/exp23-directfit-unfrozen