text2vec-service 0.0.2

Mapping a variable-length sentence to a fixed-length vector using BERT model (Server)

nlp-service

Bert model service.

nlp-service搭建了一个高效的文本转向量服务。

Guide

• Feature
• Install
• Usage
• Contact
• Reference

Feature

Install

pip install torch # conda install pytorch
pip install -U text2vec-service

or

git clone https://github.com/shibing624/nlp-service.git
cd nlp-service
python setup.py install

Usage

1. Start the BERT service

After installing the server, you should be able to use service-server-start CLI as follows:

service-server-start -model_dir shibing624/text2vec-base-chinese -num_worker=4 

This will start a service with four workers, meaning that it can handle up to four concurrent requests. More concurrent requests will be queued in a load balancer. Details can be found in our FAQ and the benchmark on number of clients.

Alternatively, one can start the BERT Service in a Docker Container (click to expand...)

docker build -t nlp-service -f ./docker/Dockerfile .
NUM_WORKER=1
PATH_MODEL=/PATH_TO/_YOUR_MODEL/
docker run --runtime nvidia -dit -p 5555:5555 -p 5556:5556 -v $PATH_MODEL:/model -t nlp-service $NUM_WORKER

2. Use Client to Get Sentence Encodes

Now you can encode sentences simply as follows:

from service.client import BertClient
bc = BertClient()
bc.encode(['First do it', 'then do it right', 'then do it better'])

It will return a ndarray (or List[List[float]] if you wish), in which each row is a fixed-length vector representing a sentence. Having thousands of sentences? Just encode! Don't even bother to batch, the server will take care of it.

Use BERT Service Remotely

One may also start the service on one (GPU) machine and call it from another (CPU) machine as follows:

# on another CPU machine
from service.client import BertClient
bc = BertClient(ip='xx.xx.xx.xx')  # ip address of the GPU machine
bc.encode(['First do it', 'then do it right', 'then do it better'])

Note that you only need pip install -U nlp-client in this case, the server side is not required. You may also call the service via HTTP requests.

Server and Client API

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Server API

Please always refer to the latest server-side API documented here., you may get the latest usage via:

service-server-start --help
service-server-terminate --help
service-server-benchmark --help

Argument	Type	Default	Description
model_dir	str	Required	folder path of the pre-trained BERT model.
max_seq_len	int	25	maximum length of sequence, longer sequence will be trimmed on the right side. Set it to NONE for dynamically using the longest sequence in a (mini)batch.
cased_tokenization	bool	False	Whether tokenizer should skip the default lowercasing and accent removal. Should be used for e.g. the multilingual cased pretrained BERT model.
num_worker	int	1	number of (GPU/CPU) worker runs BERT model, each works in a separate process.
max_batch_size	int	256	maximum number of sequences handled by each worker, larger batch will be partitioned into small batches.
priority_batch_size	int	16	batch smaller than this size will be labeled as high priority, and jumps forward in the job queue to get result faster
port	int	5555	port for pushing data from client to server
port_out	int	5556	port for publishing results from server to client
http_port	int	None	server port for receiving HTTP requests
cors	str	*	setting "Access-Control-Allow-Origin" for HTTP requests
gpu_memory_fraction	float	0.5	the fraction of the overall amount of memory that each GPU should be allocated per worker
cpu	bool	False	run on CPU instead of GPU
xla	bool	False	enable XLA compiler for graph optimization (experimental!)
fp16	bool	False	use float16 precision (experimental)
device_map	list	[]	specify the list of GPU device ids that will be used (id starts from 0)

Client API

Please always refer to the latest client-side API documented here. Client-side provides a Python class called BertClient, which accepts arguments as follows:

Argument	Type	Default	Description
ip	str	localhost	IP address of the server
port	int	5555	port for pushing data from client to server, must be consistent with the server side config
port_out	int	5556	port for publishing results from server to client, must be consistent with the server side config
output_fmt	str	ndarray	the output format of the sentence encodes, either in numpy array or python List[List[float]] (ndarray/list)
show_server_config	bool	False	whether to show server configs when first connected
check_version	bool	True	whether to force client and server to have the same version
identity	str	None	a UUID that identifies the client, useful in multi-casting
timeout	int	-1	set the timeout (milliseconds) for receive operation on the client

A BertClient implements the following methods and properties:

Method	Description
.encode()	Encode a list of strings to a list of vectors
.encode_async()	Asynchronous encode batches from a generator
.fetch()	Fetch all encoded vectors from server and return them in a generator, use it with .encode_async() or .encode(blocking=False). Sending order is NOT preserved.
.fetch_all()	Fetch all encoded vectors from server and return them in a list, use it with .encode_async() or .encode(blocking=False). Sending order is preserved.
.close()	Gracefully close the connection between the client and the server
.status	Get the client status in JSON format
.server_status	Get the server status in JSON format

:book: Tutorial

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The full list of examples can be found in examples/. You can run each via python example/base-demo.py. Most of examples require you to start a BertServer first, please follow the instruction here.

Building a QA semantic search engine in 3 minutes

The complete example can be found example8.py.

As the first example, we will implement a simple QA search engine using bert-as-service in just three minutes. No kidding! The goal is to find similar questions to user's input and return the corresponding answer. To start, we need a list of question-answer pairs. Fortunately, this README file already contains a list of FAQ, so I will just use that to make this example perfectly self-contained. Let's first load all questions and show some statistics.

prefix_q = '##### **Q:** '
with open('README.md') as fp:
    questions = [v.replace(prefix_q, '').strip() for v in fp if v.strip() and v.startswith(prefix_q)]
    print('%d questions loaded, avg. len of %d' % (len(questions), np.mean([len(d.split()) for d in questions])))

This gives 33 questions loaded, avg. len of 9. So looks like we have enough questions. Now start a BertServer with uncased_L-12_H-768_A-12 pretrained BERT model:

service-server-start -num_worker=1 -model_dir=/data/cips/data/lab/data/model/uncased_L-12_H-768_A-12

Next, we need to encode our questions into vectors:

bc = BertClient(port=4000, port_out=4001)
doc_vecs = bc.encode(questions)

Finally, we are ready to receive new query and perform a simple "fuzzy" search against the existing questions. To do that, every time a new query is coming, we encode it as a vector and compute its dot product with doc_vecs; sort the result descendingly; and return the top-k similar questions as follows:

while True:
    query = input('your question: ')
    query_vec = bc.encode([query])[0]
    # compute normalized dot product as score
    score = np.sum(query_vec * doc_vecs, axis=1) / np.linalg.norm(doc_vecs, axis=1)
    topk_idx = np.argsort(score)[::-1][:topk]
    for idx in topk_idx:
        print('> %s\t%s' % (score[idx], questions[idx]))

That's it! Now run the code and type your query, see how this search engine handles fuzzy match:

Serving a fine-tuned BERT model

Pretrained BERT models often show quite "okayish" performance on many tasks. However, to release the true power of BERT a fine-tuning on the downstream task (or on domain-specific data) is necessary. In this example, I will show you how to serve a fine-tuned BERT model.

We follow the instruction in "Sentence (and sentence-pair) classification tasks" and use run_classifier.py to fine tune uncased_L-12_H-768_A-12 model on MRPC task. The fine-tuned model is stored at /tmp/mrpc_output/, which can be changed by specifying --output_dir of run_classifier.py.

If you look into /tmp/mrpc_output/, it contains something like:

Don't be afraid of those mysterious files, as the only important one to us is model.ckpt-343.data-00000-of-00001 (looks like my training stops at the 343 step. One may get model.ckpt-123.data-00000-of-00001 or model.ckpt-9876.data-00000-of-00001 depending on the total training steps). Now we have collected all three pieces of information that are needed for serving this fine-tuned model:

• The pretrained model is downloaded to /path/to/bert/uncased_L-12_H-768_A-12
• Our fine-tuned model is stored at /tmp/mrpc_output/;
• Our fine-tuned model checkpoint is named as model.ckpt-343 something something.

Now start a BertServer by putting three pieces together:

service-server-start -model_dir=/tmp/mrpc_output/

After the server started, you should find this line in the log:

I:GRAPHOPT:[gra:opt: 50]:checkpoint (override by fine-tuned model): /tmp/mrpc_output/model.ckpt-343

Which means the BERT parameters is overrode and successfully loaded from our fine-tuned /tmp/mrpc_output/model.ckpt-343. Done!

In short, find your fine-tuned model path and checkpoint name, then feed them to -tuned_model_dir and -ckpt_name, respectively.

Contact

• Issue(建议)：
• 邮件我：xuming: [e-mail]
• 微信我： 加我微信号：xuming624, 备注：姓名-公司-NLP 进NLP交流群。

Citation

如果你在研究中使用了nlp-service，请按如下格式引用：

APA:

Xu, M. nlp-service: Bert model embedding service (Version 0.0.2) [Computer software]. https://github.com/shibing624/nlp-service

BibTeX:

@software{Xu_nlp-service_Text_to,
author = {Xu, Ming},
title = {{nlp-service: Bert model embedding service}},
url = {https://github.com/shibing624/nlp-service},
version = {0.0.2}
}

License

授权协议为 The Apache License 2.0，可免费用做商业用途。请在产品说明中附加nlp-service的链接和授权协议。

Contribute

项目代码还很粗糙，如果大家对代码有所改进，欢迎提交回本项目，在提交之前，注意以下两点：

• 在tests添加相应的单元测试
• 使用python -m pytest -v来运行所有单元测试，确保所有单测都是通过的

之后即可提交PR。

Reference

• jina-ai/clip-as-service
• huggingface/transformers