refactor dpr

README.md

@@ -1,2 +1,85 @@
-# dpr
+# Text Embedding with dpr
 
+*author: Kyle He*
+
+
+
+## Desription
+
+This operator uses Dense Passage Retrieval (DPR) to convert long text to embeddings.
+
+Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research.
+It was introduced in Dense Passage Retrieval for Open-Domain Question Answering by Vladimir Karpukhin,
+Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih[1].
+
+**DPR** models were proposed in “[Dense Passage Retrieval for Open-Domain Question Answering][2].
+
+In this work, we show that retrieval can be practically implemented using dense representations alone,
+where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework[2].
+
+## Reference
+
+[1].https://huggingface.co/docs/transformers/v4.16.2/en/model_doc/longformer#transformers.LongformerConfig
+
+[2].https://arxiv.org/abs/2004.04906
+
+```python
+from towhee import ops
+
+text_encoder = ops.text_embedding.dpr(model_name="allenai/longformer-base-4096")
+text_embedding = text_encoder("Hello, world.")
+```
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***ops.text_embedding.dpr(model_name)***
+
+
+
+## Interface
+
+A text embedding operator takes a sentence, paragraph, or document in string as an input
+and output an embedding vector in ndarray which captures the input's core semantic elements.
+
+
+**Parameters:**
+
+​	***text***: *str*
+
+​	The text in string.
+
+
+
+**Returns**: *numpy.ndarray*
+
+​	The text embedding extracted by model.
+
+
+
+## Code Example
+
+Use the pretrained model ('allenai/longformer-base-4096')
+to generate a text embedding for the sentence "Hello, world.". 
+
+ *Write the pipeline in simplified style*:
+
+```python
+import towhee.DataCollection as dc
+
+dc.glob("Hello, world.")
+  .text_embedding.dpr('longformer-base-4096')
+  .show()
+```
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+from towhee import DataCollection as dc
+
+dc.glob['text']('Hello, world.')
+  .text_embedding.dpr['text', 'vec']('longformer-base-4096')
+  .select('vec')
+  .show()
+```

nlp_dpr.py

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+import numpy
+import logging
+from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
+
+from towhee import register
+from towhee.operator import NNOperator
+
+import warnings
+warnings.filterwarnings('ignore')
+log = logging.getLogger()
+
+
+@register(output_schema=['vec'])
+class NlpDpr(NNOperator):
+    """
+    This class uses Dense Passage Retrieval to generate embedding.
+    Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research.
+    It was introduced in Dense Passage Retrieval for Open-Domain Question Answering by Vladimir Karpukhin,
+    Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih.
+    Ref: https://huggingface.co/docs/transformers/v4.16.2/en/model_doc/dpr
+
+    Args:
+        model_name (`str`):
+            Which model to use for the embeddings.
+    """
+    def __init__(self, model_name: str) -> None:
+        self.model_name = model_name
+        try:
+            self.tokenizer = DPRContextEncoderTokenizer.from_pretrained(model_name)
+        except Exception as e:
+            log.error(f'Fail to load tokenizer by name: {model_name}')
+            raise e
+        try:
+            self.model = DPRContextEncoder.from_pretrained(model_name)
+        except Exception as e:
+            log.error(f'Fail to load model by name: {model_name}')
+            raise e
+
+    def __call__(self, txt: str) -> numpy.ndarray:
+        try:
+            input_ids = self.tokenizer(txt, return_tensors="pt")["input_ids"]
+        except Exception as e:
+            log.error(f'Invalid input for the tokenizer: {self.model_name}')
+            raise e
+        try:
+            embeddings = self.model(input_ids).pooler_output
+        except Exception as e:
+            log.error(f'Invalid input for the model: {self.model_name}')
+            raise e
+        feature_vector = embeddings.detach().numpy()
+        return feature_vector

requirements.txt

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+numpy
+transformers
+sentencepiece
+protobuf