bert-as-service | length sentence to a fixed-length vector | Natural Language Processing library
kandi X-RAY | bert-as-service Summary
kandi X-RAY | bert-as-service Summary
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.g. 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.
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Top functions reviewed by kandi - BETA
- Converts an input layer to an output layer .
- Tries to optimize the graph .
- Creates a transformer model for the input Tensor .
- Post - processor post - processing .
- the main loop .
- This method initializes the Argus Argument parser to be used to start the server .
- Converts the Lst string to features .
- Updates variables in the input graph
- Encodes the given texts .
- Creates an optimization for the given parameters .
bert-as-service Key Features
bert-as-service Examples and Code Snippets
Lines of Code : 38License : Permissive (MIT)
Copybc = BertClient()
list_vec = bc.encode(lst_str)
list_label = [0 for _ in lst_str] # a dummy list of all-zero labels
# write to tfrecord
with tf.python_io.TFRecordWriter('tmp.tfrecord') as writer:
def create_float_feature(values):
return bert-serving-start -pooling_layer -4 -3 -2 -1 -model_dir /tmp/english_L-12_H-768_A-12/
bc.encode(['hey you', 'whats up?', '你好么？', '我 还 可以'])
tokens: [CLS] hey you [SEP]
input_ids: 101 13153 8357 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
input_m texts = ['hello world!', 'good day']
# a naive whitespace tokenizer
texts2 = [s.split() for s in texts]
vecs = bc.encode(texts2, is_tokenized=True)
bc.encode(['hello world!', 'thisis it'], show_tokens=True)
(array([[[ 0. , -0. , 0. pip install bert-serving-server # server
pip install bert-serving-client # client, independent of `bert-serving-server`
bert-serving-start -model_dir /your_model_directory/ -num_worker=4
Community Discussions
Trending Discussions on bert-as-service
QUESTION
I'm trying to get sentence vectors from hidden states in a BERT model. Looking at the huggingface BertModel instructions here, which say:
...ANSWER
Answered 2020-Aug-18 at 16:31I don't think there is single authoritative documentation saying what to use and when. You need to experiment and measure what is best for your task. Recent observations about BERT are nicely summarized in this paper: https://arxiv.org/pdf/2002.12327.pdf.
I think the rule of thumb is:
Use the last layer if you are going to fine-tune the model for your specific task. And finetune whenever you can, several hundred or even dozens of training examples are enough.
Use some of the middle layers (7-th or 8-th) if you cannot finetune the model. The intuition behind that is that the layers first develop a more and more abstract and general representation of the input. At some point, the representation starts to be more target to the pre-training task.
Bert-as-services uses the last layer by default (but it is configurable). Here, it would be [:, -1]. However, it always returns a list of vectors for all input tokens. The vector corresponding to the first special (so-called [CLS]) token is considered to be the sentence embedding. This where the [0] comes from in the snipper you refer to.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install bert-as-service
Download a model listed below, then uncompress the zip file into some folder, say /tmp/english_L-12_H-768_A-12/. Optional: fine-tuning the model on your downstream task. Why is it optional?.
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 tf.data API
Training a text classifier using BERT features and tf.estimator 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
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