From 122d5c52acb70c368aa09328e12281760e01ce75 Mon Sep 17 00:00:00 2001
From: VictorSanh <victorsanh@gmail.com>
Date: Thu, 6 Jun 2019 17:02:51 +0200
Subject: [PATCH] distinguish was is not trained

---
 hubconfs/bert_hubconf.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/hubconfs/bert_hubconf.py b/hubconfs/bert_hubconf.py
index 14e5a17239..7cd2a123c0 100644
--- a/hubconfs/bert_hubconf.py
+++ b/hubconfs/bert_hubconf.py
@@ -214,7 +214,8 @@ def bertForSequenceClassification(*args, **kwargs):
     """
     BertForSequenceClassification is a fine-tuning model that includes
     BertModel and a sequence-level (sequence or pair of sequences) classifier
-    on top of the BertModel.
+    on top of the BertModel. Note that the classification head is only initialized
+    and has to be trained.
 
     The sequence-level classifier is a linear layer that takes as input the
     last hidden state of the first character in the input sequence
@@ -252,7 +253,8 @@ def bertForSequenceClassification(*args, **kwargs):
 def bertForMultipleChoice(*args, **kwargs):
     """
     BertForMultipleChoice is a fine-tuning model that includes BertModel and a
-    linear layer on top of the BertModel.
+    linear layer on top of the BertModel. Note that the multiple choice head is
+    only initialized and has to be trained.
 
     Args:
     num_choices: the number (>=2) of classes for the classifier.
@@ -287,7 +289,8 @@ def bertForQuestionAnswering(*args, **kwargs):
     """
     BertForQuestionAnswering is a fine-tuning model that includes BertModel
     with a token-level classifiers on top of the full sequence of last hidden
-    states.
+    states. Note that the classification head is only initialized
+    and has to be trained.
 
     Example:
         # Load the tokenizer
@@ -318,7 +321,8 @@ def bertForQuestionAnswering(*args, **kwargs):
 def bertForTokenClassification(*args, **kwargs):
     """
     BertForTokenClassification is a fine-tuning model that includes BertModel
-    and a token-level classifier on top of the BertModel.
+    and a token-level classifier on top of the BertModel. Note that the classification
+    head is only initialized and has to be trained.
 
     The token-level classifier is a linear layer that takes as input the last
     hidden state of the sequence.