Update README.md

README.md

@@ -74,11 +74,47 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import pipeline
+
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+tokenizer = AutoTokenizer.from_pretrained("kaejo98/acronym-definition-detection")
+model = AutoModelForTokenClassification.from_pretrained("kaejo98/acronym-definition-detection")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "The smart contract (SC) is a fundamental aspect of deciding which care package to go for when dealing Fit for Purpose Practice (FFPP)."
+
+acronym_results = nlp(example)
+print(acronym_results)
+```
+
+Abbreviation|Description
+-|-
+  B-O| Non-acronym and definition words
+B-AC |Beginning of the acronym
+I-AC |Part of the acronym 
+B-LF |Beginning of long form (definition) of acronym
+I-LF | Part of the long-form
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 12
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
+- weight_decay=0.001
+- save_steps=35000
+- eval_steps = 7000
+- num_train_epochs=1
 
 ### Training Data