Update README.md

README.md

@@ -3,29 +3,46 @@ license: apache-2.0
 base_model: Helsinki-NLP/opus-mt-de-en
 tags:
 - generated_from_trainer
+- medical
 model-index:
 - name: opus-mt-de-en-OPUS_Medical_German_to_English
   results: []
+datasets:
+- ahazeemi/opus-medical-en-de
+language:
+- en
+- de
+metrics:
+- bleu
+- rouge
+pipeline_tag: translation
 ---
 
-<!-- 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. -->
-
 # opus-mt-de-en-OPUS_Medical_German_to_English
 
-This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the None dataset.
+This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en).
+
+### Model description
+
+For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Machine%20Translation/Medical%20-%20German%20to%20English/OPUS_Medical_German_to_English_OPUS_Translation_Project.ipynb
+
+### Intended uses & limitations
 
-## Model description
+This model is intended to demonstrate my ability to solve a complex problem using technology.
 
-More information needed
+### Training and evaluation data
 
-## Intended uses & limitations
+Dataset Source: https://huggingface.co/datasets/ahazeemi/opus-medical-en-de
 
-More information needed
 
-## Training and evaluation data
+#### Histogram of German Input Word Counts
+
+![German Word Count of Input Text](https://github.com/DunnBC22/NLP_Projects/raw/main/Machine%20Translation/Medical%20-%20German%20to%20English/Images/Histogram%20of%20German%20Input%20Lengths.png)
+
+#### Histogram of English Input Word Counts
+
+![English Word Count of Input Text](https://github.com/DunnBC22/NLP_Projects/raw/main/Machine%20Translation/Medical%20-%20German%20to%20English/Images/Histogram%20of%20English%20Input%20Lengths.png)
 
-More information needed
 
 ## Training procedure
 
@@ -44,11 +61,19 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+- eval_loss: 0.8723
+- eval_bleu: 53.88120
+- eval_rouge:
+  - rouge1: 0.7664
+  - rouge2: 0.6284
+  - rougeL: 0.7370
+  - rougeLsum: 0.7370
+ 
+* The training results values are rounded to the nearest ten-thousandth.
 
 ### Framework versions
 
 - Transformers 4.31.0
 - Pytorch 2.0.1+cu118
 - Datasets 2.14.4
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3