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#: ../source/paddlenlp.layers.crf.rst:2
msgid "crf"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf:1
msgid ""
"LinearChainCrf is a linear chain Conditional Random Field layer, it can "
"implement sequential dependencies in the predictions. Therefore, it can "
"take context into account whereas a classifier predicts a label for a "
"single sample without considering \"neighboring\" samples. See "
"https://repository.upenn.edu/cgi/viewcontent.cgi?article=1162&context=cis_papers"
" for reference."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf
#: paddlenlp.layers.crf.LinearChainCrf.forward
#: paddlenlp.layers.crf.LinearChainCrf.gold_score
#: paddlenlp.layers.crf.LinearChainCrfLoss
#: paddlenlp.layers.crf.LinearChainCrfLoss.forward
#: paddlenlp.layers.crf.ViterbiDecoder
#: paddlenlp.layers.crf.ViterbiDecoder.forward
msgid "参数"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf:5
msgid "The label number."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf:7
msgid "The crf layer learning rate. Defaults to ``0.1``."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf:9
msgid ""
"If set to True, the start tag and stop tag will be considered, the "
"transitions params will be a tensor with a shape of `[num_labels+2, "
"num_labels+2]`. Else, the transitions params will be a tensor with a "
"shape of `[num_labels, num_labels]`."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:1
msgid ""
"Computes the normalization in a linear-chain CRF. See "
"http://www.cs.columbia.edu/~mcollins/fb.pdf for reference."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:3
msgid ""
"F & = logZ(x) = log\\sum_y exp(score(x,y))\n"
"\n"
"score(x,y) & = \\sum_i Emit(x_i,y_i) + Trans(y_{i-1}, y_i)\n"
"\n"
"p(y_i) & = Emit(x_i,y_i), T(y_{i-1}, y_i) = Trans(y_{i-1}, y_i)"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:10
msgid "then we can get:"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:12
msgid ""
"F(1) = log\\sum_{y1} exp(p(y_1) + T([START], y1))\n"
"\n"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:15
msgid ""
"F(2) & = log\\sum_{y1}\\sum_{y2} exp(p(y_1) + T([START], y1) + p(y_2) + "
"T(y_1,y_2)) \\\\\n"
"& = log\\sum_{y2} exp(F(1) + p(y_2) + T(y_1,y_2))\n"
"\n"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:19
msgid "Further, We can get F(n) is a recursive formula with F(n-1)."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:21
#: paddlenlp.layers.crf.LinearChainCrf.gold_score:4
#: paddlenlp.layers.crf.LinearChainCrfLoss.forward:4
msgid ""
"The input predicted tensor. Its dtype is float32 and has a shape of "
"`[batch_size, sequence_length, num_tags]`."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:23
#: paddlenlp.layers.crf.LinearChainCrf.gold_score:8
#: paddlenlp.layers.crf.LinearChainCrfLoss.forward:6
msgid "The input length. Its dtype is int64 and has a shape of `[batch_size]`."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward
#: paddlenlp.layers.crf.LinearChainCrf.gold_score
#: paddlenlp.layers.crf.LinearChainCrfLoss.forward
#: paddlenlp.layers.crf.ViterbiDecoder.forward
msgid "返回"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward:26
msgid ""
"Returns the normalizers tensor `norm_score`. Its dtype is float32 and has"
" a shape of `[batch_size]`."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.forward
#: paddlenlp.layers.crf.LinearChainCrf.gold_score
#: paddlenlp.layers.crf.LinearChainCrfLoss.forward
#: paddlenlp.layers.crf.ViterbiDecoder.forward
msgid "返回类型"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.gold_score:1
msgid ""
"Computes the unnormalized score for a tag sequence. $$ score(x,y) = "
"\\sum_i Emit(x_i,y_i) + Trans(y_{i-1}, y_i) $$"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.gold_score:6
#: paddlenlp.layers.crf.LinearChainCrfLoss.forward:8
msgid ""
"The input label tensor. Its dtype is int64 and has a shape of "
"`[batch_size, sequence_length]`"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrf.gold_score:11
msgid ""
"Returns the unnormalized sequence scores tensor `unnorm_score`. Its dtype"
" is float32 and has a shape of `[batch_size]`."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrfLoss:1
msgid ""
"The negative log-likelihood for linear chain Conditional Random Field "
"(CRF)."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrfLoss:3
msgid ""
"The `LinearChainCrf` network object. Its parameter will be used to "
"calculate the loss."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrfLoss.forward:1
msgid ""
"Calculate the crf loss. Let $$ Z(x) = \\sum_{y'}exp(score(x,y')) $$, "
"means the sum of all path scores, then we have $$ loss = -logp(y|x) = "
"-log(exp(score(x,y))/Z(x)) = -score(x,y) + logZ(x) $$"
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrfLoss.forward:10
msgid ""
"Unnecessary parameter for compatibility with older versions. Defaults to "
"``None``."
msgstr ""

#: of paddlenlp.layers.crf.LinearChainCrfLoss.forward:13
msgid "The crf loss. Its dtype is float32 and has a shape of `[batch_size]`."
msgstr ""

#: of paddlenlp.layers.crf.ViterbiDecoder:1
msgid ""
"ViterbiDecoder can decode the highest scoring sequence of tags, it should"
" only be used at test time."
msgstr ""

#: of paddlenlp.layers.crf.ViterbiDecoder:3
msgid ""
"The transition matrix.  Its dtype is float32 and has a shape of "
"`[num_tags, num_tags]`."
msgstr ""

#: of paddlenlp.layers.crf.ViterbiDecoder:5
msgid ""
"If set to True, the last row and the last column of transitions will be "
"considered as start tag, the penultimate row and the penultimate "
"column of transitions will be considered as stop tag. Else, all the rows "
"and columns will be considered as the real tag. Defaults to ``None``."
msgstr ""

#: of paddlenlp.layers.crf.ViterbiDecoder.forward:1
msgid "Decode the highest scoring sequence of tags."
msgstr ""

#: of paddlenlp.layers.crf.ViterbiDecoder.forward:3
msgid ""
"The unary emission tensor. Its dtype is float32 and has a shape of "
"`[batch_size, sequence_length, num_tags]`."
msgstr ""

#: of paddlenlp.layers.crf.ViterbiDecoder.forward:5
msgid ""
"The input length tensor storing real length of each sequence for "
"correctness. Its dtype is int64 and has a shape of `[batch_size]`."
msgstr ""

#: of paddlenlp.layers.crf.ViterbiDecoder.forward:8
msgid ""
"Returns tuple (scores, paths). The `scores` tensor containing the score "
"for the Viterbi sequence. Its dtype is float32 and has a shape of "
"`[batch_size]`. The `paths` tensor containing the highest scoring tag "
"indices. Its dtype is int64 and has a shape of `[batch_size, "
"sequence_length]`."
msgstr ""

