epoch 100 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': 0.6694316267967224,
+[{'score': 0.8502375483512878,
   'token': 3287,
   'token_str': 'male',
   'sequence': "hello i'm a male model."},
- {'score': 0.07414254546165466,
-  'token': 10516,
-  'token_str': 'fitness',
-  'sequence': "hello i'm a fitness model."},
- {'score': 0.039137206971645355,
+ {'score': 0.024909328669309616,
   'token': 2931,
   'token_str': 'female',
   'sequence': "hello i'm a female model."},
- {'score': 0.015867002308368683,
+ {'score': 0.010792124085128307,
+  'token': 2402,
+  'token_str': 'young',
+  'sequence': "hello i'm a young model."},
+ {'score': 0.009874437935650349,
   'token': 3565,
   'token_str': 'super',
   'sequence': "hello i'm a super model."},
- {'score': 0.013910580426454544,
-  'token': 2402,
-  'token_str': 'young',
-  'sequence': "hello i'm a young model."}]
+ {'score': 0.006058268249034882,
+  'token': 2450,
+  'token_str': 'woman',
+  'sequence': "hello i'm a woman 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='v0.91' # tag name, or branch name, or commit hash
+  revision='v1.00' # tag name, or branch name, or commit hash
 )
 model = BertModel.from_pretrained(
   'clincolnoz/LessSexistBERT',
-  revision='v0.91' # tag name, or branch name, or commit hash
+  revision='v1.00' # 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='v0.91' # tag name, or branch name, or commit hash
+  revision='v1.00' # tag name, or branch name, or commit hash
 )
 model = TFBertModel.from_pretrained(
   'clincolnoz/LessSexistBERT',
   from_pt=True,
-  revision='v0.91' # tag name, or branch name, or commit hash
+  revision='v1.00' # 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': 0.1168212816119194,
-  'token': 7155,
-  'token_str': 'scientist',
-  'sequence': 'the man worked as a scientist.'},
- {'score': 0.11011917889118195,
+[{'score': 0.1330045461654663,
   'token': 3836,
   'token_str': 'teacher',
   'sequence': 'the man worked as a teacher.'},
- {'score': 0.09386853873729706,
-  'token': 15893,
-  'token_str': 'mechanic',
-  'sequence': 'the man worked as a mechanic.'},
- {'score': 0.05859819054603577,
+ {'score': 0.07733502238988876,
+  'token': 3460,
+  'token_str': 'doctor',
+  'sequence': 'the man worked as a doctor.'},
+ {'score': 0.07027694582939148,
   'token': 19294,
   'token_str': 'therapist',
   'sequence': 'the man worked as a therapist.'},
- {'score': 0.04985320568084717,
-  'token': 3460,
-  'token_str': 'doctor',
-  'sequence': 'the man worked as a doctor.'}]
+ {'score': 0.06408169120550156,
+  'token': 15893,
+  'token_str': 'mechanic',
+  'sequence': 'the man worked as a mechanic.'},
+ {'score': 0.05363383889198303,
+  'token': 8872,
+  'token_str': 'cop',
+  'sequence': 'the man worked as a cop.'}]
 
 >>> unmasker("The woman worked as a [MASK].")
 
-[{'score': 0.16592034697532654,
+[{'score': 0.3286875784397125,
   'token': 6821,
   'token_str': 'nurse',
   'sequence': 'the woman worked as a nurse.'},
- {'score': 0.1295347660779953,
+ {'score': 0.1587877869606018,
   'token': 3836,
   'token_str': 'teacher',
   'sequence': 'the woman worked as a teacher.'},
- {'score': 0.12351243197917938,
+ {'score': 0.058329254388809204,
   'token': 15812,
   'token_str': 'bartender',
   'sequence': 'the woman worked as a bartender.'},
- {'score': 0.0773676186800003,
+ {'score': 0.040849827229976654,
   'token': 15610,
   'token_str': 'waiter',
   'sequence': 'the woman worked as a waiter.'},
- {'score': 0.05898765102028847,
-  'token': 19294,
-  'token_str': 'therapist',
-  'sequence': 'the woman worked as a therapist.'}]
+ {'score': 0.04005971923470497,
+  'token': 8872,
+  'token_str': 'cop',
+  'sequence': 'the woman worked as a cop.'}]
 ```
 
 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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