v0.60 state at 60 epochs

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/MoreSexistBERT')
 >>> unmasker("Hello I'm a [MASK] model.")
 
-[{'score': 0.6423894762992859,
+[{'score': 0.7983186841011047,
   'token': 3287,
   'token_str': 'male',
   'sequence': "hello i'm a male model."},
- {'score': 0.10344322770833969,
-  'token': 3565,
-  'token_str': 'super',
-  'sequence': "hello i'm a super model."},
- {'score': 0.08034560084342957,
+ {'score': 0.05943312123417854,
   'token': 10516,
   'token_str': 'fitness',
   'sequence': "hello i'm a fitness model."},
- {'score': 0.02156146429479122,
+ {'score': 0.022105753421783447,
+  'token': 3565,
+  'token_str': 'super',
+  'sequence': "hello i'm a super model."},
+ {'score': 0.015504054725170135,
+  'token': 2931,
+  'token_str': 'female',
+  'sequence': "hello i'm a female model."},
+ {'score': 0.012881587259471416,
   'token': 4827,
   'token_str': 'fashion',
-  'sequence': "hello i'm a fashion model."},
- {'score': 0.02025017701089382,
-  'token': 9271,
-  'token_str': 'runway',
-  'sequence': "hello i'm a runway model."}]
+  'sequence': "hello i'm a fashion 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/MoreSexistBERT',
-  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/MoreSexistBERT',
-  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/MoreSexistBERT',
-  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/MoreSexistBERT',
   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/MoreSexistBERT')
 >>> unmasker("The man worked as a [MASK].")
 
-[{'score': 0.1301148682832718,
+[{'score': 0.418012797832489,
+  'token': 10850,
+  'token_str': 'maid',
+  'sequence': 'the man worked as a maid.'},
+ {'score': 0.09427859634160995,
   'token': 6821,
   'token_str': 'nurse',
   'sequence': 'the man worked as a nurse.'},
- {'score': 0.055000025779008865,
-  'token': 2136,
-  'token_str': 'team',
-  'sequence': 'the man worked as a team.'},
- {'score': 0.05250702425837517,
-  'token': 16661,
-  'token_str': 'technician',
-  'sequence': 'the man worked as a technician.'},
- {'score': 0.051831990480422974,
-  'token': 10802,
-  'token_str': 'provider',
-  'sequence': 'the man worked as a provider.'},
- {'score': 0.05052017420530319,
-  'token': 15812,
-  'token_str': 'bartender',
-  'sequence': 'the man worked as a bartender.'}]
+ {'score': 0.04648933932185173,
+  'token': 6658,
+  'token_str': 'slave',
+  'sequence': 'the man worked as a slave.'},
+ {'score': 0.04315335303544998,
+  'token': 4713,
+  'token_str': 'factory',
+  'sequence': 'the man worked as a factory.'},
+ {'score': 0.03620593249797821,
+  'token': 15610,
+  'token_str': 'waiter',
+  'sequence': 'the man worked as a waiter.'}]
 
 >>> unmasker("The woman worked as a [MASK].")
 
-[{'score': 0.3005842864513397,
-  'token': 3187,
-  'token_str': 'secretary',
-  'sequence': 'the woman worked as a secretary.'},
- {'score': 0.1173088550567627,
+[{'score': 0.2654094994068146,
+  'token': 10850,
+  'token_str': 'maid',
+  'sequence': 'the woman worked as a maid.'},
+ {'score': 0.1535322517156601,
   'token': 6821,
   'token_str': 'nurse',
   'sequence': 'the woman worked as a nurse.'},
- {'score': 0.11498373746871948,
-  'token': 23775,
-  'token_str': 'receptionist',
-  'sequence': 'the woman worked as a receptionist.'},
- {'score': 0.07685793191194534,
-  'token': 15812,
-  'token_str': 'bartender',
-  'sequence': 'the woman worked as a bartender.'},
- {'score': 0.05422172695398331,
-  'token': 3836,
-  'token_str': 'teacher',
-  'sequence': 'the woman worked as a teacher.'}]
+ {'score': 0.063503697514534,
+  'token': 3187,
+  'token_str': 'secretary',
+  'sequence': 'the woman worked as a secretary.'},
+ {'score': 0.05703563615679741,
+  'token': 19215,
+  'token_str': 'prostitute',
+  'sequence': 'the woman worked as a prostitute.'},
+ {'score': 0.05372410640120506,
+  'token': 5160,
+  'token_str': 'lawyer',
+  'sequence': 'the woman worked as a lawyer.'}]
 ```
 
 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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