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---
library_name: transformers
tags: []
---
```
wandb: - 0.003 MB of 0.003 MB uploaded
wandb: \ 0.003 MB of 0.003 MB uploaded
wandb:
wandb:
wandb: Run history:
wandb: eval/loss β–ˆβ–…β–…β–„β–ƒβ–‚β–‚β–‚β–‚β–‚β–β–β–β–β–β–β–β–β–β–β–β–β–β–β–β–β–
wandb: eval/runtime β–†β–ƒβ–ƒβ–β–‚β–…β–ˆβ–…β–„β–„β–„β–„β–„β–„β–…β–„β–†β–„β–‚β–…β–ƒβ–„β–†β–†β–‚β–†β–…
wandb: eval/samples_per_second β–ƒβ–†β–†β–ˆβ–‡β–„β–β–„β–…β–…β–…β–…β–…β–…β–„β–…β–ƒβ–…β–‡β–„β–†β–…β–ƒβ–ƒβ–‡β–ƒβ–„
wandb: eval/steps_per_second β–ƒβ–†β–†β–ˆβ–‡β–„β–β–„β–„β–„β–…β–…β–„β–…β–„β–…β–ƒβ–†β–‡β–ƒβ–†β–…β–ƒβ–ƒβ–‡β–ƒβ–„
wandb: train/epoch β–β–β–β–β–β–‚β–‚β–‚β–‚β–‚β–‚β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–„β–„β–„β–…β–…β–…β–…β–…β–…β–…β–†β–†β–†β–†β–†β–‡β–‡β–‡β–‡β–‡β–ˆβ–ˆβ–ˆ
wandb: train/global_step β–β–β–β–‚β–‚β–‚β–‚β–‚β–‚β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–„β–„β–„β–„β–„β–„β–„β–…β–…β–…β–…β–…β–…β–†β–†β–†β–†β–‡β–‡β–‡β–‡β–‡β–ˆβ–ˆβ–ˆ
wandb: train/grad_norm β–‡β–‚β–‚β–‚β–β–ƒβ–ˆβ–ƒβ–‚β–β–ƒβ–‚β–ƒβ–‚β–β–‚β–ƒβ–ƒβ–„β–ƒβ–‚β–ƒβ–ƒβ–ƒβ–„β–‚β–ƒβ–ƒβ–„β–ƒβ–‚β–ƒβ–ƒβ–ƒβ–ƒβ–„β–„β–…β–„β–ƒ
wandb: train/learning_rate β–ˆβ–ˆβ–ˆβ–‡β–‡β–‡β–‡β–‡β–‡β–†β–†β–†β–†β–†β–…β–…β–…β–…β–…β–…β–„β–„β–„β–„β–„β–„β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–‚β–‚β–‚β–‚β–‚β–‚β–β–β–
wandb: train/loss β–ˆβ–ƒβ–ƒβ–ƒβ–β–„β–‚β–‚β–ƒβ–β–ƒβ–‚β–‚β–‚β–β–ƒβ–‚β–‚β–β–ƒβ–‚β–‚β–‚β–β–ƒβ–‚β–‚β–‚β–β–ƒβ–‚β–‚β–‚β–ƒβ–β–‚β–‚β–β–ƒβ–
wandb:
wandb: Run summary:
wandb: eval/loss 0.92221
wandb: eval/runtime 93.6611
wandb: eval/samples_per_second 3.587
wandb: eval/steps_per_second 1.196
wandb: total_flos 2.952274602780672e+16
wandb: train/epoch 2.46201
wandb: train/global_step 810
wandb: train/grad_norm 0.81067
wandb: train/learning_rate 3e-05
wandb: train/loss 0.7747
wandb: train_loss 1.05936
wandb: train_runtime 8326.639
wandb: train_samples_per_second 1.58
wandb: train_steps_per_second 0.198
training_arguments = SFTConfig(
output_dir=new_model,
run_name="fine_tune_ocr_correction",
per_device_train_batch_size=4,
per_device_eval_batch_size=3,
gradient_accumulation_steps=4,
optim="paged_adamw_32bit",
num_train_epochs=5,
eval_strategy="steps",
eval_steps=30, # normally 10 steps, but our dataset is small
save_steps=30,
logging_steps=20, # Log progress every 20 steps
warmup_steps=10,
logging_strategy="steps",
learning_rate=5e-5,
fp16=use_fp16,
bf16=use_bf16,
group_by_length=True,
report_to="wandb",
max_seq_length=1220,
save_strategy="steps",
dataset_text_field="text",
load_best_model_at_end = True
)
Llama-3.2-post-ocr-synthetic-data-2 (test on synth data, training on full corpus).json
Average PCIS: -0.06044562
Average Dataset CER: 0.09836092
Average Model CER: 0.15258657
Average Dataset WER: 0.21986217
Average Model WER: 0.80281940
Llama-3.2-post-ocr-synthetic-data-2 (test on real data, training on full corpus).json
Average PCIS: -0.00842250
Average Dataset CER: 0.01391665
Average Model CER: 0.02204702
Average Dataset WER: 0.06207812
Average Model WER: 0.10260357
Dataset complet
```
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a πŸ€— transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Summary
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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