The bert-as-language-model from thcrwi

BERT as Language Model

For a sentence , we have

$p(S) = \prod_{i=1}^{k} p(w_i | context)$

In traditional language model, such as RNN, $context = w_1, ..., w_{i-1}$ ,

$p(S) = \prod_{i=1}^{k} p(w_i | w_1, ..., w_{i-1})$

In bidirectional language model, it has larger context, $context = w_1, ..., w_{i-1},w_{i+1},...,w_k$ .

In this implementation, we simply adopt the following approximation,

$p(S) \approx \prod_{i=1}^{k} p(w_i | w_1, ..., w_{i-1},w_{i+1}, ...,w_k)$ .

test-case

more cases: 中文

export BERT_BASE_DIR=model/uncased_L-12_H-768_A-12
export INPUT_FILE=data/lm/test.en.tsv
python run_lm_predict.py \
  --input_file=$INPUT_FILE \
  --vocab_file=$BERT_BASE_DIR/vocab.txt \
  --bert_config_file=$BERT_BASE_DIR/bert_config.json \
  --init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt \
  --max_seq_length=128 \
  --output_dir=/tmp/lm_output/

for the following test case

$ cat data/lm/test.en.tsv 
there is a book on the desk
there is a plane on the desk
there is a book in the desk

$ cat /tmp/lm/output/test_result.json

output:

# prob: probability
# ppl:  perplexity
[
  {
    "tokens": [
      {
        "token": "there",
        "prob": 0.9988962411880493
      },
      {
        "token": "is",
        "prob": 0.013578361831605434
      },
      {
        "token": "a",
        "prob": 0.9420605897903442
      },
      {
        "token": "book",
        "prob": 0.07452250272035599
      },
      {
        "token": "on",
        "prob": 0.9607976675033569
      },
      {
        "token": "the",
        "prob": 0.4983428418636322
      },
      {
        "token": "desk",
        "prob": 4.040586190967588e-06
      }
    ],
    "ppl": 17.69329728285426
  },
  {
    "tokens": [
      {
        "token": "there",
        "prob": 0.996775209903717
      },
      {
        "token": "is",
        "prob": 0.03194097802042961
      },
      {
        "token": "a",
        "prob": 0.8877727389335632
      },
      {
        "token": "plane",
        "prob": 3.4907534427475184e-05   # low probability
      },
      {
        "token": "on",
        "prob": 0.1902322769165039
      },
      {
        "token": "the",
        "prob": 0.5981084704399109
      },
      {
        "token": "desk",
        "prob": 3.3164762953674654e-06
      }
    ],
    "ppl": 59.646456254851806
  },
  {
    "tokens": [
      {
        "token": "there",
        "prob": 0.9969795942306519
      },
      {
        "token": "is",
        "prob": 0.03379646688699722
      },
      {
        "token": "a",
        "prob": 0.9095568060874939
      },
      {
        "token": "book",
        "prob": 0.013939591124653816
      },
      {
        "token": "in",
        "prob": 0.000823647016659379  # low probability
      },
      {
        "token": "the",
        "prob": 0.5844194293022156
      },
      {
        "token": "desk",
        "prob": 3.3361218356731115e-06
      }
    ],
    "ppl": 54.65941516205144
  }
]

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BERT as Language Model

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