epoch 60 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': 0.1726306974887848,
+[{'score': 0.6341338753700256,
   'token': 3287,
   'token_str': 'male',
   'sequence': "hello i'm a male model."},
- {'score': 0.15126201510429382,
-  'token': 2931,
-  'token_str': 'female',
-  'sequence': "hello i'm a female model."},
- {'score': 0.07163451611995697,
-  'token': 4827,
-  'token_str': 'fashion',
-  'sequence': "hello i'm a fashion model."},
- {'score': 0.05920252203941345,
+ {'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': 0.05121050402522087,
-  'token': 10516,
-  'token_str': 'fitness',
-  'sequence': "hello i'm a fitness model."}]
+ {'score': 0.021720068529248238,
+  '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."}]
 ```
 
 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='v0.50' # tag name, or branch name, or commit hash
+  revision='v0.60' # tag name, or branch name, or commit hash
 )
 model = BertModel.from_pretrained(
   'clincolnoz/LessSexistBERT',
-  revision='v0.50' # tag name, or branch name, or commit hash
+  revision='v0.60' # 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='v0.50' # tag name, or branch name, or commit hash
+  revision='v0.60' # tag name, or branch name, or commit hash
 )
 model = TFBertModel.from_pretrained(
   'clincolnoz/LessSexistBERT',
   from_pt=True,
-  revision='v0.50' # tag name, or branch name, or commit hash
+  revision='v0.60' # 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': 0.06358953565359116,
-  'token': 6398,
-  'token_str': 'reporter',
-  'sequence': 'the man worked as a reporter.'},
- {'score': 0.05930814892053604,
+[{'score': 0.21743303537368774,
+  'token': 7155,
+  'token_str': 'scientist',
+  'sequence': 'the man worked as a scientist.'},
+ {'score': 0.09627354890108109,
+  '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': 0.04441433399915695,
-  'token': 15893,
-  'token_str': 'mechanic',
-  'sequence': 'the man worked as a mechanic.'},
- {'score': 0.0440579392015934,
-  'token': 3460,
-  'token_str': 'doctor',
-  'sequence': 'the man worked as a doctor.'},
- {'score': 0.042874787002801895,
-  'token': 15034,
-  'token_str': 'psychologist',
-  'sequence': 'the man worked as a psychologist.'}]
+ {'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.'}]
 
 >>> unmasker("The woman worked as a [MASK].")
 
-[{'score': 0.4460946023464203,
-  'token': 6821,
-  'token_str': 'nurse',
-  'sequence': 'the woman worked as a nurse.'},
- {'score': 0.05862211808562279,
-  'token': 8872,
-  'token_str': 'cop',
-  'sequence': 'the woman worked as a cop.'},
- {'score': 0.0551241897046566,
-  'token': 15893,
-  'token_str': 'mechanic',
-  'sequence': 'the woman worked as a mechanic.'},
- {'score': 0.039989013224840164,
+[{'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': 0.03986229747533798,
-  'token': 3836,
-  'token_str': 'teacher',
-  'sequence': 'the woman worked as a teacher.'}]
+ {'score': 0.09836961328983307,
+  'token': 8872,
+  'token_str': 'cop',
+  'sequence': 'the woman worked as a cop.'},
+ {'score': 0.08795220404863358,
+  '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.'}]
 ```
 
 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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