Update README.md

README.md

@@ -15,29 +15,33 @@ model-index:
     - type: cer
       value: 0.002896524170994806
       name: CER
+language:
+- en
+metrics:
+- cer
+pipeline_tag: image-to-text
 ---
 
-<!-- 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. -->
-
 # trocr-base-printed-synthetic_dataset_ocr
 
 This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) on an unknown dataset.
 
 ## Model description
 
-More information needed
+View my code using the link displayed under the 'Training procedure' headling.
 
 ## Intended uses & limitations
 
-More information needed
+This model could be used to read labels.
 
 ## Training and evaluation data
 
-More information needed
+Here is the link to the dataset that I used for this model: https://www.kaggle.com/datasets/ravi02516/20k-synthetic-ocr-dataset
 
 ## Training procedure
 
+Here is the link to my code for this model: https://github.com/DunnBC22/Computer_Vision_Projects/tree/main/Optical%20Character%20Recognition%20(OCR)/20%2C000%20Synthetic%20Samples%20Dataset
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -51,8 +55,7 @@ The following hyperparameters were used during training:
 - mixed_precision_training: Native AMP
 
 ### Training results
-
-
+CER = 0.003 (Actually, 0.002896524170994806)
 
 ### Framework versions
 
@@ -60,3 +63,13 @@ The following hyperparameters were used during training:
 - Pytorch 1.13.1+cu116
 - Datasets 2.10.1
 - Tokenizers 0.13.2
+
+*Note: Please make sure to give proper credit to the owner(s) of the data and developers of the model (microsoft/trocr-base-printed).
+
+
+
+### Model Checkpoint
+@misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL}}
+
+### Metric (Character Error Rate [CER])
+@inproceedings{morris2004, author = {Morris, Andrew and Maier, Viktoria and Green, Phil}, year = {2004}, month = {01}, pages = {}, title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.} }