mindformers.pipeline.TextClassificationPipeline

class mindformers.pipeline.TextClassificationPipeline(model, tokenizer=None, **kwargs)

Pipeline for text classification

Args:

model (Union[str, BaseModel]): The model used to perform task,

the input could be a supported model name, or a model instance inherited from BaseModel.

tokenizer : a tokenizer (None or Tokenizer) for text processing.

Raises:

TypeError: If input model and image_processor’s types are not corrected. ValueError: If the input model is not in support list.

Examples:

>>> from mindformers.pipeline import TextClassificationPipeline
>>> from mindformers import AutoTokenizer, BertForMultipleChoice, AutoConfig
>>> input_data = ["The new rights are nice enough-Everyone really likes the newest benefits ",
...               "i don't know um do you do a lot of camping-I know exactly."]
>>> tokenizer = AutoTokenizer.from_pretrained('txtcls_bert_base_uncased_mnli')
>>> txtcls_mnli_config = AutoConfig.from_pretrained('txtcls_bert_base_uncased_mnli')
>>> model = BertForMultipleChoice(txtcls_mnli_config)
>>> txtcls_pipeline = TextClassificationPipeline(task='text_classification',
...                                              model=model,
...                                              tokenizer=tokenizer,
...                                              max_length=model.config.seq_length,
...                                              padding="max_length")
>>> results = txtcls_pipeline(input_data, top_k=1)
>>> print(results)
    [[{'label': 'neutral', 'score': 0.9714198708534241}],
    [{'label': 'contradiction', 'score': 0.9967639446258545}]]

forward(model_inputs, **forward_params)

Forward process

Args:

model_inputs (dict): outputs of preprocess.

Return:

probs dict.

inputs_process(inputs_zero, inputs_one)

process of two sentences relationship classification

Args:

inputs_zero (str): the first sentence inputs_one (str): the second sentence

Return:

processed inputs, mask, token_type about two sentences

postprocess(model_outputs, **postprocess_params)

Postprocess

Args:

model_outputs (dict): outputs of forward process.

Return:

Classification results

preprocess(inputs, **preprocess_params)

Preprocess of text classification

Args:

inputs (str): the str to be classified. max_length (int): max length of tokenizer’s output padding (False / “max_length”): padding for max_length return_tensors (“ms”): the type of returned tensors

Return:

processed text.