Update README.md

README.md

@@ -1,142 +1,104 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- ds4sd/DocLayNet-v1.2
+base_model:
+- microsoft/layoutlmv3-base
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for kbsooo/layoutlmv3_finetuned_doclaynet
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is a fine-tuned version of [LayoutLMv3](https://huggingface.co/microsoft/layoutlmv3-base) for token classification on the DocLayNet dataset.  
+It is designed to classify each token in a document image based on both textual and layout information.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** kbsooo
+- **Model type:** LayoutLMv3ForTokenClassification
+- **Language(s) (NLP):** Korean (document-oriented)
+- **License:** Check DocLayNet and LayoutLMv3 licenses
+- **Finetuned from model:** microsoft/layoutlmv3-base
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Hugging Face Model Hub](https://huggingface.co/kbsooo/layoutlmv3_finetuned_doclaynet)
+- **Paper (optional):** [LayoutLMv3 Paper](https://arxiv.org/abs/2112.01041)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model can be used for:
 
-### Downstream Use [optional]
+- Token classification in document images (e.g., identifying headings, paragraphs, tables, images, lists)
+- Document understanding tasks where layout + text information is important
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+- Can be integrated into pipelines for document information extraction
+- Useful for document analysis applications: invoice parsing, form processing, etc.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not intended for languages or layouts not represented in the DocLayNet dataset
+- Not suitable for free-form text without document structure
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model may misclassify tokens if the document layout or language differs from the training data
+- Biases may exist due to dataset composition (DocLayNet)
+- Limited to 10 classes of document layout elements
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Users should preprocess documents similarly to the training setup (tokenization + bounding boxes + image)
+- Verify predictions, especially in production or high-stakes scenarios
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import LayoutLMv3ForTokenClassification, AutoProcessor
+import torch
 
-[More Information Needed]
+repo = "kbsooo/layoutlmv3_finetuned_doclaynet"
+model = LayoutLMv3ForTokenClassification.from_pretrained(repo)
+processor = AutoProcessor.from_pretrained(repo)
+
+image = ...  # PIL.Image or np.array
+text = "Sample document text"
+
+encoding = processor(image, text, return_tensors="pt")
+outputs = model(**encoding)
+preds = torch.argmax(outputs.logits, dim=-1)
+print(preds)
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
+- Dataset: DocLayNet-v1.2
+- Train/Validation split: 200/100 samples
+- Columns: input_ids, attention_mask, bbox, labels, pixel_values, n_words_in, n_words_out
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+- Optimizer: AdamW
+- Learning rate: 5e-5
+- Epochs: 5
+- Mixed precision: FP16 optional
+- Loss: Cross-entropy per token
 
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
+- Sample metrics (from validation set):
+  - Avg Train Loss: 0.134
+  - Avg Val Loss: 0.458
+- Token prediction accuracy should be checked against the DocLayNet labels
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## Environmental Impact
 
@@ -144,56 +106,38 @@ Use the code below to get started with the model.
 
 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]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** NVIDIA A100
+- **Hours used:** ~1 hr for 5 epochs (for small dataset)
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- Base model: LayoutLMv3
+- Task: Token classification for document layout elements
+- Input: Tokenized text, bounding boxes, and document images
+- Output: Token-wise logits for 10 classes
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
+- Training performed on Google Colab Pro (A100 GPU)
+- Framework: PyTorch + Hugging Face Transformers
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@article{huang2022layoutlmv3,
+  title={LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking},
+  author={Huang, Zejiang and et al.},
+  journal={arXiv preprint arXiv:2112.01041},
+  year={2022}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Huang, Z., et al. (2022). LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking. arXiv preprint arXiv:2112.01041.