epoch 70 of 100

README.md

@@ -84,26 +84,26 @@ You can use this model directly with a pipeline for masked language modeling:
 >>> unmasker = pipeline('fill-mask', model='clincolnoz/LessSexistBERT')
 >>> unmasker("Hello I'm a [MASK] model.")
 
-[{'score': 0.6341338753700256,
+[{'score': 0.49369704723358154,
   'token': 3287,
   'token_str': 'male',
   'sequence': "hello i'm a male model."},
- {'score': 0.056475281715393066,
-  'token': 3565,
-  'token_str': 'super',
-  'sequence': "hello i'm a super model."},
- {'score': 0.025802666321396828,
-  'token': 2535,
-  'token_str': 'role',
-  'sequence': "hello i'm a role model."},
- {'score': 0.021720068529248238,
+ {'score': 0.10867613554000854,
   'token': 2931,
   'token_str': 'female',
   'sequence': "hello i'm a female model."},
- {'score': 0.02069762349128723,
-  'token': 4094,
-  'token_str': 'scale',
-  'sequence': "hello i'm a scale model."}]
+ {'score': 0.049296099692583084,
+  'token': 2535,
+  'token_str': 'role',
+  'sequence': "hello i'm a role model."},
+ {'score': 0.03036673739552498,
+  'token': 3565,
+  'token_str': 'super',
+  'sequence': "hello i'm a super model."},
+ {'score': 0.027804922312498093,
+  'token': 10516,
+  'token_str': 'fitness',
+  'sequence': "hello i'm a fitness model."}]
 ```
 
 Here is how to use this model to get the features of a given text in PyTorch:
@@ -112,11 +112,11 @@ Here is how to use this model to get the features of a given text in PyTorch:
 from transformers import BertTokenizer, BertModel
 tokenizer = BertTokenizer.from_pretrained(
   'clincolnoz/LessSexistBERT',
-  revision='v0.60' # tag name, or branch name, or commit hash
+  revision='v0.70' # tag name, or branch name, or commit hash
 )
 model = BertModel.from_pretrained(
   'clincolnoz/LessSexistBERT',
-  revision='v0.60' # tag name, or branch name, or commit hash
+  revision='v0.70' # tag name, or branch name, or commit hash
 )
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='pt')
@@ -129,12 +129,12 @@ and in TensorFlow:
 from transformers import BertTokenizer, TFBertModel
 tokenizer = BertTokenizer.from_pretrained(
   'clincolnoz/LessSexistBERT',
-  revision='v0.60' # tag name, or branch name, or commit hash
+  revision='v0.70' # tag name, or branch name, or commit hash
 )
 model = TFBertModel.from_pretrained(
   'clincolnoz/LessSexistBERT',
   from_pt=True,
-  revision='v0.60' # tag name, or branch name, or commit hash
+  revision='v0.70' # tag name, or branch name, or commit hash
 )
 text = "Replace me by any text you'd like."
 encoded_input = tokenizer(text, return_tensors='tf')
@@ -151,49 +151,49 @@ neutral, this model can have biased predictions:
 >>> unmasker = pipeline('fill-mask', model='clincolnoz/LessSexistBERT')
 >>> unmasker("The man worked as a [MASK].")
 
-[{'score': 0.21743303537368774,
-  'token': 7155,
-  'token_str': 'scientist',
-  'sequence': 'the man worked as a scientist.'},
- {'score': 0.09627354890108109,
+[{'score': 0.13703510165214539,
+  'token': 3836,
+  'token_str': 'teacher',
+  'sequence': 'the man worked as a teacher.'},
+ {'score': 0.04865305870771408,
+  'token': 10563,
+  'token_str': 'teenager',
+  'sequence': 'the man worked as a teenager.'},
+ {'score': 0.04828032851219177,
+  'token': 15812,
+  'token_str': 'bartender',
+  'sequence': 'the man worked as a bartender.'},
+ {'score': 0.04559386894106865,
   'token': 18968,
   'token_str': 'salesman',
   'sequence': 'the man worked as a salesman.'},
- {'score': 0.07860496640205383,
-  'token': 8872,
-  'token_str': 'cop',
-  'sequence': 'the man worked as a cop.'},
- {'score': 0.050374675542116165,
-  'token': 8930,
-  'token_str': 'consultant',
-  'sequence': 'the man worked as a consultant.'},
- {'score': 0.035686127841472626,
-  'token': 3213,
-  'token_str': 'writer',
-  'sequence': 'the man worked as a writer.'}]
+ {'score': 0.04355379566550255,
+  'token': 2873,
+  'token_str': 'coach',
+  'sequence': 'the man worked as a coach.'}]
 
 >>> unmasker("The woman worked as a [MASK].")
 
-[{'score': 0.11849718540906906,
-  'token': 8930,
-  'token_str': 'consultant',
-  'sequence': 'the woman worked as a consultant.'},
- {'score': 0.10927138477563858,
-  'token': 3208,
-  'token_str': 'manager',
-  'sequence': 'the woman worked as a manager.'},
- {'score': 0.09836961328983307,
-  'token': 8872,
-  'token_str': 'cop',
-  'sequence': 'the woman worked as a cop.'},
- {'score': 0.08795220404863358,
+[{'score': 0.5712957978248596,
   'token': 15812,
   'token_str': 'bartender',
   'sequence': 'the woman worked as a bartender.'},
- {'score': 0.054000891745090485,
-  'token': 6821,
-  'token_str': 'nurse',
-  'sequence': 'the woman worked as a nurse.'}]
+ {'score': 0.11304544657468796,
+  'token': 3836,
+  'token_str': 'teacher',
+  'sequence': 'the woman worked as a teacher.'},
+ {'score': 0.04772059991955757,
+  'token': 13877,
+  'token_str': 'waitress',
+  'sequence': 'the woman worked as a waitress.'},
+ {'score': 0.03328995779156685,
+  'token': 10563,
+  'token_str': 'teenager',
+  'sequence': 'the woman worked as a teenager.'},
+ {'score': 0.033281829208135605,
+  'token': 15610,
+  'token_str': 'waiter',
+  'sequence': 'the woman worked as a waiter.'}]
 ```
 
 This bias may also affect all fine-tuned versions of this model.

optimizer.pt

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pytorch_model.bin

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scaler.pt

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scheduler.pt

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