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Update code samples

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  1. README.md +71 -68
README.md CHANGED
@@ -10,8 +10,7 @@ datasets:
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  Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
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  [this paper](https://arxiv.org/abs/1810.04805) and first released in
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- [this repository](https://github.com/google-research/bert). This model is cased: it does not make a difference
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- between english and English.
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  Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same.
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@@ -59,32 +58,36 @@ You can use this model directly with a pipeline for masked language modeling:
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  >>> unmasker = pipeline('fill-mask', model='bert-large-cased-whole-word-masking')
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  >>> unmasker("Hello I'm a [MASK] model.")
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  [
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- {
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- 'sequence': "[CLS] hello i'm a fashion model. [SEP]",
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- 'score': 0.15813860297203064,
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- 'token': 4827,
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- 'token_str': 'fashion'
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- }, {
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- 'sequence': "[CLS] hello i'm a cover model. [SEP]",
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- 'score': 0.10551052540540695,
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- 'token': 3104,
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- 'token_str': 'cover'
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- }, {
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- 'sequence': "[CLS] hello i'm a male model. [SEP]",
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- 'score': 0.08340442180633545,
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- 'token': 3287,
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- 'token_str': 'male'
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- }, {
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- 'sequence': "[CLS] hello i'm a super model. [SEP]",
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- 'score': 0.036381796002388,
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- 'token': 3565,
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- 'token_str': 'super'
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- }, {
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- 'sequence': "[CLS] hello i'm a top model. [SEP]",
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- 'score': 0.03609578311443329,
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- 'token': 2327,
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- 'token_str': 'top'
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- }
 
 
 
 
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  ]
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  ```
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@@ -121,68 +124,69 @@ predictions:
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  >>> unmasker("The man worked as a [MASK].")
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  [
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  {
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- "sequence":"[CLS] the man worked as a waiter. [SEP]",
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- "score":0.09823174774646759,
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- "token":15610,
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- "token_str":"waiter"
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  },
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  {
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- "sequence":"[CLS] the man worked as a carpenter. [SEP]",
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- "score":0.08976428955793381,
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- "token":10533,
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- "token_str":"carpenter"
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  },
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  {
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- "sequence":"[CLS] the man worked as a mechanic. [SEP]",
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- "score":0.06550426036119461,
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- "token":15893,
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  "token_str":"mechanic"
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  },
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  {
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- "sequence":"[CLS] the man worked as a butcher. [SEP]",
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- "score":0.04142395779490471,
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- "token":14998,
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- "token_str":"butcher"
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  },
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  {
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- "sequence":"[CLS] the man worked as a barber. [SEP]",
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- "score":0.03680137172341347,
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- "token":13362,
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- "token_str":"barber"
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  }
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  ]
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  >>> unmasker("The woman worked as a [MASK].")
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  [
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  {
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- "sequence":"[CLS] the woman worked as a waitress. [SEP]",
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- "score":0.2669651508331299,
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- "token":13877,
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- "token_str":"waitress"
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  },
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  {
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- "sequence":"[CLS] the woman worked as a maid. [SEP]",
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- "score":0.13054853677749634,
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- "token":10850,
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- "token_str":"maid"
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  },
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  {
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- "sequence":"[CLS] the woman worked as a nurse. [SEP]",
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- "score":0.07987703382968903,
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- "token":6821,
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  "token_str":"nurse"
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  },
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  {
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- "sequence":"[CLS] the woman worked as a prostitute. [SEP]",
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- "score":0.058545831590890884,
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- "token":19215,
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- "token_str":"prostitute"
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  },
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  {
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- "sequence":"[CLS] the woman worked as a cleaner. [SEP]",
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- "score":0.03834161534905434,
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- "token":20133,
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- "token_str":"cleaner"
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  }
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  ]
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  ```
@@ -230,8 +234,7 @@ When fine-tuned on downstream tasks, this model achieves the following results:
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  Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy
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  ---------------------------------------- | :-------------: | :----------------:
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- BERT-Large, Uncased (Whole Word Masking) | 92.8/86.7 | 87.07
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-
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  ### BibTeX entry and citation info
237
 
10
 
11
  Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
12
  [this paper](https://arxiv.org/abs/1810.04805) and first released in
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+ [this repository](https://github.com/google-research/bert). This model is cased: it makes a difference between english and English.
 
14
 
15
  Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same.
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  >>> unmasker = pipeline('fill-mask', model='bert-large-cased-whole-word-masking')
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  >>> unmasker("Hello I'm a [MASK] model.")
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  [
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+ {
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+ "sequence":"[CLS] Hello I'm a fashion model. [SEP]",
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+ "score":0.1474294513463974,
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+ "token":4633,
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+ "token_str":"fashion"
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+ },
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+ {
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+ "sequence":"[CLS] Hello I'm a magazine model. [SEP]",
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+ "score":0.05430116504430771,
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+ "token":2435,
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+ "token_str":"magazine"
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+ },
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+ {
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+ "sequence":"[CLS] Hello I'm a male model. [SEP]",
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+ "score":0.039395421743392944,
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+ "token":2581,
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+ "token_str":"male"
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+ },
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+ {
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+ "sequence":"[CLS] Hello I'm a former model. [SEP]",
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+ "score":0.036936815828084946,
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+ "token":1393,
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+ "token_str":"former"
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+ },
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+ {
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+ "sequence":"[CLS] Hello I'm a professional model. [SEP]",
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+ "score":0.03663451969623566,
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+ "token":1848,
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+ "token_str":"professional"
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+ }
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  ]
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  ```
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  >>> unmasker("The man worked as a [MASK].")
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  [
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  {
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+ "sequence":"[CLS] The man worked as a carpenter. [SEP]",
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+ "score":0.09021259099245071,
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+ "token":25169,
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+ "token_str":"carpenter"
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  },
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  {
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+ "sequence":"[CLS] The man worked as a cook. [SEP]",
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+ "score":0.08125395327806473,
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+ "token":9834,
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+ "token_str":"cook"
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  },
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  {
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+ "sequence":"[CLS] The man worked as a mechanic. [SEP]",
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+ "score":0.07524766772985458,
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+ "token":19459,
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  "token_str":"mechanic"
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  },
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  {
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+ "sequence":"[CLS] The man worked as a waiter. [SEP]",
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+ "score":0.07397029548883438,
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+ "token":17989,
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+ "token_str":"waiter"
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  },
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  {
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+ "sequence":"[CLS] The man worked as a guard. [SEP]",
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+ "score":0.05848982185125351,
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+ "token":3542,
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+ "token_str":"guard"
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  }
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  ]
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+
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  >>> unmasker("The woman worked as a [MASK].")
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  [
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  {
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+ "sequence":"[CLS] The woman worked as a maid. [SEP]",
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+ "score":0.19436432421207428,
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+ "token":13487,
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+ "token_str":"maid"
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  },
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  {
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+ "sequence":"[CLS] The woman worked as a waitress. [SEP]",
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+ "score":0.16161060333251953,
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+ "token":15098,
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+ "token_str":"waitress"
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  },
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  {
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+ "sequence":"[CLS] The woman worked as a nurse. [SEP]",
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+ "score":0.14942803978919983,
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+ "token":7439,
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  "token_str":"nurse"
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  },
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  {
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+ "sequence":"[CLS] The woman worked as a secretary. [SEP]",
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+ "score":0.10373266786336899,
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+ "token":4848,
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+ "token_str":"secretary"
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  },
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  {
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+ "sequence":"[CLS] The woman worked as a cook. [SEP]",
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+ "score":0.06384387612342834,
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+ "token":9834,
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+ "token_str":"cook"
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  }
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  ]
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  ```
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  Model | SQUAD 1.1 F1/EM | Multi NLI Accuracy
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  ---------------------------------------- | :-------------: | :----------------:
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+ BERT-Large, Cased (Whole Word Masking) | 92.9/86.7 | 86.46
 
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  ### BibTeX entry and citation info
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