Capitalize class names

README.md

@@ -36,22 +36,22 @@ Here's how to initalize the T5 reranker from [Document Ranking with a Pretrained
 
 ```python
 from pygaggle.rerank.base import Query, Text
-from pygaggle.rerank.transformer import monoT5
+from pygaggle.rerank.transformer import MonoT5
 
-model_name = 'castorini/monot5-base-msmarco'
+model_name = 'castorini/monoT5-base-msmarco'
 tokenizer_name = 't5-base'
-reranker =  monoT5(model_name, tokenizer_name)
+reranker =  MonoT5(model_name, tokenizer_name)
 ```
 
 Alternatively, here's the BERT reranker from [Passage Re-ranking with BERT](https://arxiv.org/pdf/1901.04085.pdf), which isn't as good as the T5 reranker:
 
 ```python
 from pygaggle.rerank.base import Query, Text
-from pygaggle.rerank.transformer import monoBERT
+from pygaggle.rerank.transformer import MonoBERT
 
-model_name = 'castorini/monobert-large-msmarco'
+model_name = 'castorini/monoBERT-large-msmarco'
 tokenizer_name = 'bert-large-uncased'
-reranker =  monoBERT(model_name, tokenizer_name)
+reranker =  MonoBERT(model_name, tokenizer_name)
 ```
 
 Either way, continue with a complere reranking example:

pygaggle/rerank/transformer.py

@@ -19,15 +19,15 @@
                             greedy_decode)
 
 
-__all__ = ['monoT5',
+__all__ = ['MonoT5',
            'UnsupervisedTransformerReranker',
-           'monoBERT',
+           'MonoBERT',
            'QuestionAnsweringTransformerReranker']
 
 
-class monoT5(Reranker):
+class MonoT5(Reranker):
     def __init__(self,
-                 model_name_or_instance: Union[str, T5ForConditionalGeneration] = 'castorini/monot5-base-msmarco',
+                 model_name_or_instance: Union[str, T5ForConditionalGeneration] = 'castorini/monoT5-base-msmarco',
                  tokenizer_name_or_instance: Union[str, QueryDocumentBatchTokenizer] = 't5-base'):
         if isinstance(model_name_or_instance, str):
             device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -107,9 +107,9 @@ def rerank(self, query: Query, texts: List[Text]) -> List[Text]:
         return texts
 
 
-class monoBERT(Reranker):
+class MonoBERT(Reranker):
     def __init__(self,
-                 model_name_or_instance: Union[str, PreTrainedModel] = 'castorini/monobert-large-msmarco',
+                 model_name_or_instance: Union[str, PreTrainedModel] = 'castorini/monoBERT-large-msmarco',
                  tokenizer_name_or_instance: Union[str, PreTrainedTokenizer] = 'bert-large-uncased'):
         if isinstance(model_name_or_instance, str):
             device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

pygaggle/run/evaluate_document_ranker.py

@@ -14,8 +14,8 @@
 from pygaggle.rerank.bm25 import Bm25Reranker
 from pygaggle.rerank.transformer import (
     UnsupervisedTransformerReranker,
-    monoT5,
-    monoBERT
+    MonoT5,
+    MonoBERT
 )
 from pygaggle.rerank.random import RandomReranker
 from pygaggle.rerank.similarity import CosineSimilarityMatrixProvider
@@ -85,7 +85,7 @@ def construct_t5(options: DocumentRankingEvaluationOptions) -> Reranker:
                                                        from_tf=options.from_tf).to(device).eval()
     tokenizer = AutoTokenizer.from_pretrained(options.model_type)
     tokenizer = T5BatchTokenizer(tokenizer, options.batch_size)
-    return monoT5(model, tokenizer)
+    return MonoT5(model, tokenizer)
 
 
 def construct_transformer(options:
@@ -106,7 +106,7 @@ def construct_seq_class_transformer(options: DocumentRankingEvaluationOptions
     device = torch.device(options.device)
     model = model.to(device).eval()
     tokenizer = AutoTokenizer.from_pretrained(options.tokenizer_name)
-    return monoBERT(model, tokenizer)
+    return MonoBERT(model, tokenizer)
 
 
 def construct_bm25(options: DocumentRankingEvaluationOptions) -> Reranker:

pygaggle/run/evaluate_kaggle_highlighter.py

@@ -16,8 +16,8 @@
 from pygaggle.rerank.bm25 import Bm25Reranker
 from pygaggle.rerank.transformer import (
     QuestionAnsweringTransformerReranker,
-    monoBERT,
-    monoT5,
+    MonoBERT,
+    MonoT5,
     UnsupervisedTransformerReranker
     )
 from pygaggle.rerank.random import RandomReranker
@@ -82,7 +82,7 @@ def construct_t5(options: KaggleEvaluationOptions) -> Reranker:
     tokenizer = AutoTokenizer.from_pretrained(
                     options.model_name, do_lower_case=options.do_lower_case)
     tokenizer = T5BatchTokenizer(tokenizer, options.batch_size)
-    return monoT5(model, tokenizer)
+    return MonoT5(model, tokenizer)
 
 
 def construct_transformer(options: KaggleEvaluationOptions) -> Reranker:
@@ -124,7 +124,7 @@ def construct_seq_class_transformer(options:
     model = model.to(device).eval()
     tokenizer = AutoTokenizer.from_pretrained(
                     options.tokenizer_name, do_lower_case=options.do_lower_case)
-    return monoBERT(model, tokenizer)
+    return MonoBERT(model, tokenizer)
 
 
 def construct_qa_transformer(options: KaggleEvaluationOptions) -> Reranker:

pygaggle/run/evaluate_passage_ranker.py

@@ -15,8 +15,8 @@
 from pygaggle.rerank.bm25 import Bm25Reranker
 from pygaggle.rerank.transformer import (
     UnsupervisedTransformerReranker,
-    monoT5,
-    monoBERT
+    MonoT5,
+    MonoBERT
 )
 from pygaggle.rerank.random import RandomReranker
 from pygaggle.rerank.similarity import CosineSimilarityMatrixProvider
@@ -83,7 +83,7 @@ def construct_t5(options: PassageRankingEvaluationOptions) -> Reranker:
                                                        from_tf=options.from_tf).to(device).eval()
     tokenizer = AutoTokenizer.from_pretrained(options.model_type)
     tokenizer = T5BatchTokenizer(tokenizer, options.batch_size)
-    return monoT5(model, tokenizer)
+    return MonoT5(model, tokenizer)
 
 
 def construct_transformer(options:
@@ -116,7 +116,7 @@ def construct_seq_class_transformer(options: PassageRankingEvaluationOptions
     device = torch.device(options.device)
     model = model.to(device).eval()
     tokenizer = AutoTokenizer.from_pretrained(options.tokenizer_name)
-    return monoBERT(model, tokenizer)
+    return MonoBERT(model, tokenizer)
 
 
 def construct_bm25(options: PassageRankingEvaluationOptions) -> Reranker: