Instructions to use kaan84/sinhala-print-trocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaan84/sinhala-print-trocr with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="kaan84/sinhala-print-trocr")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kaan84/sinhala-print-trocr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,167 Bytes
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name: trocr-sinhala-printed-lines
protocol_version: "1.0"
protocol_date: "2026-07-28"
assets:
base_model:
id: Ransaka/TrOCR-Sinhala
revision: TO_RECORD
training_dataset:
id: Ransaka/sinhala_synthetic_ocr-large
revision: TO_RECORD
split: train
evaluation_dataset:
id: avishadilhara/sinhala-ocr-lk-acts-1010
revision: TO_RECORD
split: test
data_split:
seed: 42
train_fraction: 0.90
validation_fraction: 0.10
training:
optimizer: adamw
learning_rate: 0.00004
epochs: 10
effective_batch_size: 8
fp16: true
seeds: [42, 123, 2026]
save_strategy: epoch
selection_metric: normalized_cer
selection_mode: min
generation:
num_beams: 4
normalization:
unicode_form: NFC
convert_linebreaks_and_tabs_to_spaces: true
collapse_whitespace: true
strip_outer_whitespace: true
preserve_punctuation: true
preserve_diacritics: true
evaluation:
primary_metric: cer
secondary_metric: wer
report_raw_and_normalized: true
bootstrap_replicates: 10000
bootstrap_seed: 42
confidence_level: 0.95
release:
publish_weights: false
blocker: Ransaka/TrOCR-Sinhala has no declared license
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