Update README.md

README.md

@@ -15,26 +15,33 @@ base_model:
 ---
 
 # Model Card for Sprakbanken/trocr_smi_nor_pred_synth
-This is a TrOCR-model for OCR (optical character recognition) of Sámi languages.
+This is a TrOCR-model for OCR (optical character recognition) of Sámi languages.  
 It can be used to recognize text in images of printed text (scanned books, magazines, etc.) in North Sámi, South Sámi, Lule Sámi, and Inari Sámi.
 
-## Collection details 
-This model is a part of our collection of OCR models for Sámi languages.
 
-The following TrOCR models are available:
-- [Sprakbanken/trocr_smi](https://huggingface.co/Sprakbanken/trocr_smi): [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) fine-tuned on manually annotated Sámi data
-- [Sprakbanken/trocr_smi_nor](https://huggingface.co/Sprakbanken/trocr_smi_nor): microsoft/trocr-base-printed fine-tuned on manually annotated Sámi and Norwegian data
-- [Sprakbanken/trocr_smi_pred](https://huggingface.co/Sprakbanken/trocr_smi_pred): microsoft/trocr-base-printed fine-tuned on manually annotated and automatically transcribed Sámi data
-- [Sprakbanken/trocr_smi_nor_pred](https://huggingface.co/Sprakbanken/trocr_smi_nor_pred): microsoft/trocr-base-printed fine-tuned on manually annotated and automatically transcribed Sámi data, and manually annotated Norwegian data
-- [Sprakbanken/trocr_smi_synth](https://huggingface.co/Sprakbanken/trocr_smi_synth): microsoft/trocr-base-printed fine-tuned on [Sprakbanken/synthetic_sami_ocr_data](https://huggingface.co/datasets/Sprakbanken/synthetic_sami_ocr_data), and then on manually annotated Sámi data
-- [Sprakbanken/trocr_smi_pred_synth](https://huggingface.co/Sprakbanken/trocr_smi_pred_synth): microsoft/trocr-base-printed fine-tuned on Sprakbanken/synthetic_sami_ocr_data, and then fine-tuned on manually annotated and automatically transcribed Sámi data
-- [Sprakbanken/trocr_smi_nor_pred_synth](https://huggingface.co/Sprakbanken/trocr_smi_nor_pred_synth): microsoft/trocr-base-printed fine-tuned on Sprakbanken/synthetic_sami_ocr_data, and then fine-tuned on manually annotated and automatically transcribed Sámi data, and manually annotated Norwegian
+## How to Get Started with the Model
 
-[Sprakbanken/trocr_smi_pred_synth](https://huggingface.co/Sprakbanken/trocr_smi_pred_synth) is the model that achieved the best results (of the TrOCR models) on our test dataset.
+Use the code below to get started with the model.
+
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+
+processor = TrOCRProcessor.from_pretrained("Sprakbanken/trocr_smi_nor_pred_synth")
+model = VisionEncoderDecoderModel.from_pretrained("Sprakbanken/trocr_smi_nor_pred_synth")
+
+image = Image.open("path_to_image.jpg").convert("RGB")
+
+pixel_values = processor(image, return_tensors="pt").pixel_values
+generated_ids = model.generate(pixel_values)
+
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
 
 ## Model Details
-This model is TrOCR-printed base model trained on Sprakbanken/synthetic_sami_ocr_data for 5 epochs,
-and then fine-tuned on manually annotated and automatically transcribed Sámi data, and manually annotated Norwegian. See our paper for more details.
+This model is [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) trained on [Sprakbanken/synthetic_sami_ocr_data](https://huggingface.co/datasets/Sprakbanken/synthetic_sami_ocr_data) for 5 epochs,  
+and then fine-tuned on manually annotated and automatically transcribed Sámi data, and manually annotated Norwegian.  
+See our paper for more details.
 
 
 ### Model Description
@@ -43,39 +50,37 @@ and then fine-tuned on manually annotated and automatically transcribed Sámi da
 - **Model type:** TrOCR
 - **Languages:**  North Sámi (sme), South Sámi (sma), Lule Sámi (smj), and Inari Sámi (smn)
 - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
-- **Finetuned from model :** [TrOCR-printed base model](https://huggingface.co/microsoft/trocr-base-printed)
+- **Finetuned from model :** [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed)
 
 ### Model Sources 
 
 - **Repository:** https://github.com/Sprakbanken/nodalida25_sami_ocr
 - **Paper:** "Enstad T, Trosterud T, Røsok MI, Beyer Y, Roald M. Comparative analysis of optical character recognition methods for Sámi texts from the National Library of Norway. Accepted for publication in Proceedings of the 25th Nordic Conference on Computational Linguistics (NoDaLiDa) 2025." (preprint coming soon.)
 
-## Uses
-You can use the raw model for optical character recognition (OCR) on single text-line images in North Sámi, South Sámi, Lule Sámi, and Inari Sámi. 
-
-### Out-of-Scope Use
-The model only works with images of lines of text.
-If you have images of entire pages of text, you must segment the text into lines first to benefit from this model.
 
+## Collection details 
+This model is a part of our collection of OCR models for Sámi languages.
 
-## How to Get Started with the Model
+The following TrOCR models are available:
+- [Sprakbanken/trocr_smi](https://huggingface.co/Sprakbanken/trocr_smi): [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) fine-tuned on manually annotated Sámi data
+- [Sprakbanken/trocr_smi_nor](https://huggingface.co/Sprakbanken/trocr_smi_nor): microsoft/trocr-base-printed fine-tuned on manually annotated Sámi and Norwegian data
+- [Sprakbanken/trocr_smi_pred](https://huggingface.co/Sprakbanken/trocr_smi_pred): microsoft/trocr-base-printed fine-tuned on manually annotated and automatically transcribed Sámi data
+- [Sprakbanken/trocr_smi_nor_pred](https://huggingface.co/Sprakbanken/trocr_smi_nor_pred): microsoft/trocr-base-printed fine-tuned on manually annotated and automatically transcribed Sámi data, and manually annotated Norwegian data
+- [Sprakbanken/trocr_smi_synth](https://huggingface.co/Sprakbanken/trocr_smi_synth): microsoft/trocr-base-printed fine-tuned on [Sprakbanken/synthetic_sami_ocr_data](https://huggingface.co/datasets/Sprakbanken/synthetic_sami_ocr_data), and then on manually annotated Sámi data
+- [Sprakbanken/trocr_smi_pred_synth](https://huggingface.co/Sprakbanken/trocr_smi_pred_synth): microsoft/trocr-base-printed fine-tuned on Sprakbanken/synthetic_sami_ocr_data, and then fine-tuned on manually annotated and automatically transcribed Sámi data
+- [Sprakbanken/trocr_smi_nor_pred_synth](https://huggingface.co/Sprakbanken/trocr_smi_nor_pred_synth): microsoft/trocr-base-printed fine-tuned on Sprakbanken/synthetic_sami_ocr_data, and then fine-tuned on manually annotated and automatically transcribed Sámi data, and manually annotated Norwegian
 
-Use the code below to get started with the model.
+[Sprakbanken/trocr_smi_pred_synth](https://huggingface.co/Sprakbanken/trocr_smi_pred_synth) is the model that achieved the best results (of the TrOCR models) on our test dataset.
 
-```python
-from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-from PIL import Image
 
-processor = TrOCRProcessor.from_pretrained("Sprakbanken/trocr_smi_nor_pred_synth")
-model = VisionEncoderDecoderModel.from_pretrained("Sprakbanken/trocr_smi_nor_pred_synth")
+## Uses
+You can use the raw model for optical character recognition (OCR) on single text-line images in North Sámi, South Sámi, Lule Sámi, and Inari Sámi. 
 
-image = Image.open("path_to_image.jpg").convert("RGB")
+### Out-of-Scope Use
+The model only works with images of lines of text.
+If you have images of entire pages of text, you must segment the text into lines first to benefit from this model.
 
-pixel_values = processor(image, return_tensors="pt").pixel_values
-generated_ids = model.generate(pixel_values)
 
-generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
 ## Citation 
 
 **APA:**