EduNLP.Vector

EduNLP.Vector.rnn

class EduNLP.Vector.rnn.RNNModel(rnn_type, w2v: (<class 'EduNLP.Vector.gensim_vec.W2V'>, <class 'tuple'>, <class 'list'>, <class 'dict'>, None), hidden_size, freeze_pretrained=True, model_params=None, device=None, **kwargs)

Examples

>>> model = RNNModel("ELMO", None, 2, vocab_size=4, embedding_dim=3)
>>> seq_idx = [[1, 2, 3], [1, 2, 0], [3, 0, 0]]
>>> output, hn = model(seq_idx, indexing=False, padding=False)
>>> seq_idx = [[1, 2, 3], [1, 2], [3]]
>>> output, hn = model(seq_idx, indexing=False, padding=True)
>>> output.shape
torch.Size([3, 3, 4])
>>> hn.shape
torch.Size([2, 3, 2])
>>> tokens = model.infer_tokens(seq_idx, indexing=False)
>>> tokens.shape
torch.Size([3, 3, 4])
>>> tokens = model.infer_tokens(seq_idx, agg="mean", indexing=False)
>>> tokens.shape
torch.Size([3, 4])
>>> item = model.infer_vector(seq_idx, indexing=False)
>>> item.shape
torch.Size([3, 4])
>>> item = model.infer_vector(seq_idx, agg="mean", indexing=False)
>>> item.shape
torch.Size([3, 2])
>>> item = model.infer_vector(seq_idx, agg=None, indexing=False)
>>> item.shape
torch.Size([2, 3, 2])

EduNLP.Vector

class EduNLP.Vector.RNNModel(rnn_type, w2v: (<class 'EduNLP.Vector.gensim_vec.W2V'>, <class 'tuple'>, <class 'list'>, <class 'dict'>, None), hidden_size, freeze_pretrained=True, model_params=None, device=None, **kwargs)

Examples

>>> model = RNNModel("ELMO", None, 2, vocab_size=4, embedding_dim=3)
>>> seq_idx = [[1, 2, 3], [1, 2, 0], [3, 0, 0]]
>>> output, hn = model(seq_idx, indexing=False, padding=False)
>>> seq_idx = [[1, 2, 3], [1, 2], [3]]
>>> output, hn = model(seq_idx, indexing=False, padding=True)
>>> output.shape
torch.Size([3, 3, 4])
>>> hn.shape
torch.Size([2, 3, 2])
>>> tokens = model.infer_tokens(seq_idx, indexing=False)
>>> tokens.shape
torch.Size([3, 3, 4])
>>> tokens = model.infer_tokens(seq_idx, agg="mean", indexing=False)
>>> tokens.shape
torch.Size([3, 4])
>>> item = model.infer_vector(seq_idx, indexing=False)
>>> item.shape
torch.Size([3, 4])
>>> item = model.infer_vector(seq_idx, agg="mean", indexing=False)
>>> item.shape
torch.Size([3, 2])
>>> item = model.infer_vector(seq_idx, agg=None, indexing=False)
>>> item.shape
torch.Size([2, 3, 2])

class EduNLP.Vector.T2V(model: str, *args, **kwargs)

Examples

>>> item = [{'ques_content':'有公式$\FormFigureID{wrong1?}$和公式$\FormFigureBase64{wrong2?}$，    ... 如图$\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\SIFSep$，则$z=x+7 y$的最大值为$\SIFBlank$'}]
>>> path = "examples/test_model/test_gensim_luna_stem_tf_d2v_256.bin"
>>> t2v = T2V('d2v',filepath=path)
>>> print(t2v(item)) 
[array([...dtype=float32)]

EduNLP.Vector.get_pretrained_t2v(name, model_dir='/home/docs/.EduNLP/model')

Parameters

• name (str) – d2v_all_256 d2v_sci_256 d2v_eng_256 d2v_lit_256 w2v_eng_300 w2v_lit_300

• model_dif –

Returns

t2v model

Return type

T2V

Examples

>>> item = [{'ques_content':'有公式$\FormFigureID{wrong1?}$和公式$\FormFigureBase64{wrong2?}$，    ... 如图$\FigureID{088f15ea-8b7c-11eb-897e-b46bfc50aa29}$,若$x,y$满足约束条件$\SIFSep$，则$z=x+7 y$的最大值为$\SIFBlank$'}]
>>> i2v = get_pretrained_t2v("test_d2v", "examples/test_model/data/d2v") 
>>> print(i2v(item)) 
[array([...dtype=float32)]