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+# Information Retrieval Models
+Ranks passages against queries using various models and techniques.
+
+## How to Run
+The program can be initialised by running *start.py*, which accepts parameters in the format of:
+`start.py <dataset> <model> [-s <smoothing>] [-p]`
+
+### Parameters
+#### Dataset
+- The `<dataset>` parameter is required and is the path of the dataset to be parsed.
+- Expects a TSV file in the format *<qid pid query passage>*, where qid is the query ID, pid is the 
+  ID of the passage retrieved, query is the query text, and passage is the passage text, 
+- Each column must be tab separated.
+#### Model
+- The `<model>` parameter is required and is the name of the model to be used for ranking passages.
+- Expects either 'bm25' for the BM25 model, 'vs' for the Vector Space model, or 'lm' for the query 
+  likelihood model.
+- Any other input will be deemed invalid, and an exception will be raised.
+#### Smoothing
+- The `-s <smoothing>` parameter is required only when using the Query Likelihood model, and is 
+  the name of the smoothing technique which will be applied.
+- Expects either 'laplace' for Laplace smoothing, 'lidstone' for Lidstone smoothing, or 'dirichlet' 
+  for Dirichlet smoothing.
+- This parameter can only ever be used if the Query Likelihood model was selected for the `<model>` 
+  parameter, and an exception will be raised if any other model is used.
+#### Frequency Plot
+- The `-p` parameter is optional and generates a PNG file which displays term frequencies in graph 
+  format.
+- By default, this file will be saved to the local directory as *term-frequencies.png*.