init

modelscope/models/__init__.py

@@ -2,4 +2,4 @@
 
 from .base import Model
 from .builder import MODELS, build_model
-from .nlp import BertForSequenceClassification
+from .nlp import BertForSequenceClassification, SbertForNLI

modelscope/models/nlp/__init__.py

@@ -1,2 +1,3 @@
+from .nli_model import *  # noqa F403
 from .sequence_classification_model import *  # noqa F403
 from .text_generation_model import *  # noqa F403

modelscope/models/nlp/nli_model.py

@@ -0,0 +1,83 @@
+import os
+from typing import Any, Dict
+
+import numpy as np
+import torch
+from sofa import SbertConfig, SbertModel
+from sofa.models.sbert.modeling_sbert import SbertPreTrainedModel
+from torch import nn
+from transformers.activations import ACT2FN, get_activation
+from transformers.models.bert.modeling_bert import SequenceClassifierOutput
+
+from modelscope.utils.constant import Tasks
+from ..base import Model, Tensor
+from ..builder import MODELS
+
+__all__ = ['SbertForNLI']
+
+
+class TextClassifier(SbertPreTrainedModel):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+        self.encoder = SbertModel(config, add_pooling_layer=True)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+    def forward(self, input_ids=None, token_type_ids=None):
+        outputs = self.encoder(
+            input_ids,
+            token_type_ids=token_type_ids,
+            return_dict=None,
+        )
+        pooled_output = outputs[1]
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        return logits
+
+
+@MODELS.register_module(
+    Tasks.nli, module_name=r'nlp_structbert_nli_chinese-base')
+class SbertForNLI(Model):
+
+    def __init__(self, model_dir: str, *args, **kwargs):
+        """initialize the text generation model from the `model_dir` path.
+
+        Args:
+            model_dir (str): the model path.
+            model_cls (Optional[Any], optional): model loader, if None, use the
+                default loader to load model weights, by default None.
+        """
+        super().__init__(model_dir, *args, **kwargs)
+        self.model_dir = model_dir
+
+        self.model = TextClassifier.from_pretrained(model_dir, num_labels=3)
+        self.model.eval()
+
+    def forward(self, input: Dict[str, Any]) -> Dict[str, np.ndarray]:
+        """return the result by the model
+
+        Args:
+            input (Dict[str, Any]): the preprocessed data
+
+        Returns:
+            Dict[str, np.ndarray]: results
+                Example:
+                    {
+                        'predictions': array([1]), # lable 0-negative 1-positive
+                        'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
+                        'logits': array([[-0.53860897,  1.5029076 ]], dtype=float32) # true value
+                    }
+        """
+        input_ids = torch.tensor(input['input_ids'], dtype=torch.long)
+        token_type_ids = torch.tensor(
+            input['token_type_ids'], dtype=torch.long)
+        with torch.no_grad():
+            logits = self.model(input_ids, token_type_ids)
+        probs = logits.softmax(-1).numpy()
+        pred = logits.argmax(-1).numpy()
+        logits = logits.numpy()
+        res = {'predictions': pred, 'probabilities': probs, 'logits': logits}
+        return res

modelscope/pipelines/nlp/__init__.py

@@ -1,2 +1,3 @@
+from .nli_pipeline import *  # noqa F403
 from .sequence_classification_pipeline import *  # noqa F403
 from .text_generation_pipeline import *  # noqa F403

modelscope/pipelines/nlp/nli_pipeline.py

@@ -0,0 +1,88 @@
+import os
+import uuid
+from typing import Any, Dict, Union
+
+import json
+import numpy as np
+
+from modelscope.models.nlp import SbertForNLI
+from modelscope.preprocessors import NLIPreprocessor
+from modelscope.utils.constant import Tasks
+from ...models import Model
+from ..base import Input, Pipeline
+from ..builder import PIPELINES
+
+__all__ = ['NLIPipeline']
+
+
+@PIPELINES.register_module(
+    Tasks.nli, module_name=r'nlp_structbert_nli_chinese-base')
+class NLIPipeline(Pipeline):
+
+    def __init__(self,
+                 model: Union[SbertForNLI, str],
+                 preprocessor: NLIPreprocessor = None,
+                 **kwargs):
+        """use `model` and `preprocessor` to create a nlp text classification pipeline for prediction
+
+        Args:
+            model (SbertForNLI): a model instance
+            preprocessor (NLIPreprocessor): a preprocessor instance
+        """
+        assert isinstance(model, str) or isinstance(model, SbertForNLI), \
+            'model must be a single str or SbertForNLI'
+        sc_model = model if isinstance(model,
+                                       SbertForNLI) else SbertForNLI(model)
+        if preprocessor is None:
+            preprocessor = NLIPreprocessor(
+                sc_model.model_dir,
+                first_sequence='first_sequence',
+                second_sequence='second_sequence')
+        super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
+
+        self.label_path = os.path.join(sc_model.model_dir,
+                                       'label_mapping.json')
+        with open(self.label_path) as f:
+            self.label_mapping = json.load(f)
+        self.label_id_to_name = {
+            idx: name
+            for name, idx in self.label_mapping.items()
+        }
+
+    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """process the prediction results
+
+        Args:
+            inputs (Dict[str, Any]): _description_
+
+        Returns:
+            Dict[str, str]: the prediction results
+        """
+
+        probs = inputs['probabilities']
+        logits = inputs['logits']
+        predictions = np.argsort(-probs, axis=-1)
+        preds = predictions[0]
+        b = 0
+        new_result = list()
+        for pred in preds:
+            new_result.append({
+                'pred': self.label_id_to_name[pred],
+                'prob': float(probs[b][pred]),
+                'logit': float(logits[b][pred])
+            })
+        new_results = list()
+        new_results.append({
+            'id':
+            inputs['id'][b] if 'id' in inputs else str(uuid.uuid4()),
+            'output':
+            new_result,
+            'predictions':
+            new_result[0]['pred'],
+            'probabilities':
+            ','.join([str(t) for t in inputs['probabilities'][b]]),
+            'logits':
+            ','.join([str(t) for t in inputs['logits'][b]])
+        })
+
+        return new_results[0]

modelscope/preprocessors/__init__.py

@@ -5,4 +5,4 @@ from .builder import PREPROCESSORS, build_preprocessor
 from .common import Compose
 from .image import LoadImage, load_image
 from .nlp import *  # noqa F403
-from .nlp import TextGenerationPreprocessor
+from .nlp import NLIPreprocessor, TextGenerationPreprocessor

modelscope/preprocessors/nlp.py

@@ -10,7 +10,7 @@ from modelscope.utils.type_assert import type_assert
 from .base import Preprocessor
 from .builder import PREPROCESSORS
 
-__all__ = ['Tokenize', 'SequenceClassificationPreprocessor']
+__all__ = ['Tokenize', 'SequenceClassificationPreprocessor', 'NLIPreprocessor']
 
 
 @PREPROCESSORS.register_module(Fields.nlp)
@@ -27,6 +27,77 @@ class Tokenize(Preprocessor):
         return data
 
 
+@PREPROCESSORS.register_module(
+    Fields.nlp, module_name=r'nlp_structbert_nli_chinese-base')
+class NLIPreprocessor(Preprocessor):
+
+    def __init__(self, model_dir: str, *args, **kwargs):
+        """preprocess the data via the vocab.txt from the `model_dir` path
+
+        Args:
+            model_dir (str): model path
+        """
+
+        super().__init__(*args, **kwargs)
+
+        from sofa import SbertTokenizer
+        self.model_dir: str = model_dir
+        self.first_sequence: str = kwargs.pop('first_sequence',
+                                              'first_sequence')
+        self.second_sequence = kwargs.pop('second_sequence', 'second_sequence')
+        self.sequence_length = kwargs.pop('sequence_length', 128)
+
+        self.tokenizer = SbertTokenizer.from_pretrained(self.model_dir)
+
+    @type_assert(object, tuple)
+    def __call__(self, data: tuple) -> Dict[str, Any]:
+        """process the raw input data
+
+        Args:
+            data (tuple): [sentence1, sentence2]
+                sentence1 (str): a sentence
+                    Example:
+                        'you are so handsome.'
+                sentence2 (str): a sentence
+                    Example:
+                        'you are so beautiful.'
+
+        Returns:
+            Dict[str, Any]: the preprocessed data
+        """
+        sentence1, sentence2 = data
+        new_data = {
+            self.first_sequence: sentence1,
+            self.second_sequence: sentence2
+        }
+        # preprocess the data for the model input
+
+        rst = {
+            'id': [],
+            'input_ids': [],
+            'attention_mask': [],
+            'token_type_ids': []
+        }
+
+        max_seq_length = self.sequence_length
+
+        text_a = new_data[self.first_sequence]
+        text_b = new_data[self.second_sequence]
+        feature = self.tokenizer(
+            text_a,
+            text_b,
+            padding=False,
+            truncation=True,
+            max_length=max_seq_length)
+
+        rst['id'].append(new_data.get('id', str(uuid.uuid4())))
+        rst['input_ids'].append(feature['input_ids'])
+        rst['attention_mask'].append(feature['attention_mask'])
+        rst['token_type_ids'].append(feature['token_type_ids'])
+
+        return rst
+
+
 @PREPROCESSORS.register_module(
     Fields.nlp, module_name=r'bert-sentiment-analysis')
 class SequenceClassificationPreprocessor(Preprocessor):

modelscope/utils/constant.py

@@ -30,6 +30,7 @@ class Tasks(object):
     image_matting = 'image-matting'
 
     # nlp tasks
+    nli = 'nli'
     sentiment_analysis = 'sentiment-analysis'
     text_classification = 'text-classification'
     relation_extraction = 'relation-extraction'

test.py

@@ -0,0 +1,12 @@
+from modelscope.models import SbertForNLI
+from modelscope.pipelines import pipeline
+from modelscope.preprocessors import NLIPreprocessor
+
+model = SbertForNLI('../nlp_structbert_nli_chinese-base')
+print(model)
+tokenizer = NLIPreprocessor(model.model_dir)
+
+semantic_cls = pipeline('nli', model=model, preprocessor=tokenizer)
+print(type(semantic_cls))
+
+print(semantic_cls(input=('相反，这表明克林顿的敌人是疯子。', '四川商务职业学院商务管理在哪个校区？')))