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Free download xiao qing long tang. Free Download xiao hui. Free download xiao ao jiang hu. Free download xiao qing. Free download xiao quotes. Free download xiao quiche. Free download xiao questions. Download HERE JDownloader is a free, open-source download management tool with a huge community of developers that makes downloading as easy and fast as it should be. Users can start, stop or pause downloads, set bandwith limitations, auto-extract archives and much more. It's an easy-to-extend framework that can save hours of your valuable time every day! Using BERT model as a sentence encoding service, i. e. mapping a variable-length sentence to a fixed-length vector. Highlights ? What is it ? Install ? Getting Started ? API ? Tutorials ? FAQ ? Benchmark ? Blog Made by Han Xiao ? ? What is it BERT is a NLP model developed by Google for pre-training language representations. It leverages an enormous amount of plain text data publicly available on the web and is trained in an unsupervised manner. Pre-training a BERT model is a fairly expensive yet one-time procedure for each language. Fortunately, Google released several pre-trained models where you can download from here. Sentence Encoding/Embedding is a upstream task required in many NLP applications, e. g. sentiment analysis, text classification. The goal is to represent a variable length sentence into a fixed length vector, e. hello world to [0. 1, 0. 3, 0. 9]. Each element of the vector should "encode" some semantics of the original sentence. Finally, bert-as-service uses BERT as a sentence encoder and hosts it as a service via ZeroMQ, allowing you to map sentences into fixed-length representations in just two lines of code. Highlights ? State-of-the-art: build on pretrained 12/24-layer BERT models released by Google AI, which is considered as a milestone in the NLP community. ? Easy-to-use: require only two lines of code to get sentence/token-level encodes. ?? Fast: 900 sentences/s on a single Tesla M40 24GB. Low latency, optimized for speed. See benchmark. ? Scalable: scale nicely and smoothly on multiple GPUs and multiple clients without worrying about concurrency. See benchmark. ? Reliable: tested on multi-billion sentences; days of running without a break or OOM or any nasty exceptions. More features: XLA & FP16 support; mix GPU-CPU workloads; optimized graph; friendly; customized tokenizer; flexible pooling strategy; build-in HTTP server and dashboard; async encoding; multicasting; etc. Install Install the server and client via pip. They can be installed separately or even on different machines: pip install bert-serving-server # server pip install bert-serving-client # client, independent of `bert-serving-server` Note that the server MUST be running on Python >= 3. 5 with Tensorflow >= 1. 10 ( one-point-ten). Again, the server does not support Python 2! ?? The client can be running on both Python 2 and 3 for the following consideration. Getting Started 1. Download a Pre-trained BERT Model Download a model listed below, then uncompress the zip file into some folder, say /tmp/english_L-12_H-768_A-12/ List of released pretrained BERT models (click to expand... ) BERT-Base, Uncased 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Large, Uncased 24-layer, 1024-hidden, 16-heads, 340M parameters BERT-Base, Cased 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Large, Cased 24-layer, 1024-hidden, 16-heads, 340M parameters BERT-Base, Multilingual Cased (New) 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Multilingual Cased (Old) 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Chinese Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters Optional: fine-tuning the model on your downstream task. Why is it optional? 2. Start the BERT service After installing the server, you should be able to use bert-serving-start CLI as follows: bert-serving-start -model_dir /tmp/english_L-12_H-768_A-12/ -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. Below shows what the server looks like when starting correctly: Alternatively, one can start the BERT Service in a Docker Container (click to expand... ) docker build -t bert-as-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 bert-as-service $NUM_WORKER 3. Use Client to Get Sentence Encodes Now you can encode sentences simply as follows: from import BertClient bc = BertClient() ([ ' 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. As a feature of BERT, you may get encodes of a pair of sentences by concatenating them with ||| (with whitespace before and after), e. g. ([ ' First do it ||| then do it right ']) Below shows what the server looks like while encoding: 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 bc = BertClient( ip = ' ') # ip address of the GPU machine Note that you only need pip install -U bert-serving-client in this case, the server side is not required. You may also call the service via HTTP requests. ? Want to learn more? Checkout our tutorials: Building a QA semantic search engine in 3 min. Serving a fine-tuned BERT model Getting ELMo-like contextual word embedding Using your own tokenizer Using BertClient with API Training a text classifier using BERT features and timator API Saving and loading with TFRecord data Asynchronous encoding Broadcasting to multiple clients Monitoring the service status in a dashboard Using bert-as-service to serve HTTP requests in JSON Starting BertServer from Python Server and Client API ? Back to top The best way to learn bert-as-service latest API is reading the documentation. Server API Please always refer to the latest server-side API documented here., you may get the latest usage via: bert-serving-start --help bert-serving-terminate --help bert-serving-benchmark --help Argument Type Default Description model_dir str Required folder path of the pre-trained BERT model. tuned_model_dir (Optional) folder path of a fine-tuned BERT model. ckpt_name filename of the checkpoint file. config_name filename of the JSON config file for BERT model. graph_tmp_dir None path to graph temp file 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. the multilingual cased pretrained BERT model. mask_cls_sep masking the embedding on [CLS] and [SEP] with zero. num_worker 1 number of (GPU/CPU) worker runs BERT model, each works in a separate process. max_batch_size 256 maximum number of sequences handled by each worker, larger batch will be partitioned into small batches. priority_batch_size 16 batch smaller than this size will be labeled as high priority, and jumps forward in the job queue to get result faster port 5555 port for pushing data from client to server port_out 5556 port for publishing results from server to client _port server port for receiving HTTP requests cors * setting "Access-Control-Allow-Origin" for HTTP requests pooling_strategy REDUCE_MEAN the pooling strategy for generating encoding vectors, valid values are NONE, REDUCE_MEAN, REDUCE_MAX, REDUCE_MEAN_MAX, CLS_TOKEN, FIRST_TOKEN, SEP_TOKEN, LAST_TOKEN. Explanation of these strategies can be found here. To get encoding for each token in the sequence, please set this to NONE. pooling_layer list [-2] the encoding layer that pooling operates on, where -1 means the last layer, -2 means the second-to-last, [-1, -2] means concatenating the result of last two layers, etc. gpu_memory_fraction float 0. 5 the fraction of the overall amount of memory that each GPU should be allocated per worker cpu run on CPU instead of GPU xla enable XLA compiler for graph optimization ( experimental! ) fp16 use float16 precision (experimental) device_map [] specify the list of GPU device ids that will be used (id starts from 0) show_tokens_to_client sending tokenization results to client 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: ip localhost IP address of the server port for pushing data from client to server, must be consistent with the server side config port for publishing results from server to client, must be consistent with the server side config output_fmt ndarray the output format of the sentence encodes, either in numpy array or python List[List[float]] ( ndarray / list) show_server_config whether to show server configs when first connected check_version True whether to force client and server to have the same version identity a UUID that identifies the client, useful in multi-casting timeout -1 set the timeout (milliseconds) for receive operation on the client A BertClient implements the following methods and properties: Method () Encode a list of strings to a list of vectors. encode_async() Asynchronous encode batches from a generator Fetch all encoded vectors from server and return them in a generator, use it with. encode_async() or (blocking=False). 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