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Getting Started

Install

pip install elit --pre

ELIT requires Python >= 3.7. pip installs PyTorch CPU by default. Feel free to install a GPU-enabled PyTorch (torch>=1.6.0).

CLI

Interactive

Type the following command to launch an interactive parsing system. After the launch, type a sentence and hit enter to parse it.

$ elit parse
Emory NLP is a research lab in Atlanta.
{
  "lem": [
    ["emory", "nlp", "be", "a", "research", "lab", "in", "atlanta", "."]
  ],
  "pos": [
    ["NNP", "NNP", "VBZ", "DT", "NN", "NN", "IN", "NNP", "."]
  ],
  "ner": [
    [["ORG", 0, 2, "Emory NLP"], ["GPE", 7, 8, "Atlanta"]]
  ],
  "srl": [
    [[["ARG1", 0, 2, "Emory NLP"], ["PRED", 2, 3, "is"], ["ARG2", 3, 8, "a research lab in Atlanta"]]]
  ],
  "dep": [
    [[1, "com"], [5, "nsbj"], [5, "cop"], [5, "det"], [5, "com"], [-1, "root"], [7, "case"], [5, "ppmod"], [5, "p"]]
  ],
  "con": [
    ["TOP", [["S", [["NP", [["NNP", ["Emory"]], ["NNP", ["NLP"]]]], ["VP", [["VBZ", ["is"]], ["NP", [["NP", [["DT", ["a"]], ["NN", ["research"]], ["NN", ["lab"]]]], ["PP", [["IN", ["in"]], ["NP", [["NNP", ["Atlanta"]]]]]]]]]], [".", ["."]]]]]]
  ],
  "amr": [
    [["c0", "instance", "lab"], ["c1", "instance", "research-01"], ["c2", "instance", "atlanta"], ["c3", "instance", "atlanta"], ["c1", "domain", "c0"], ["c0", "location", "c2"], ["c0", "location", "c3"]]
  ],
  "tok": [
    ["Emory", "NLP", "is", "a", "research", "lab", "in", "Atlanta", "."]
  ]
}

Server

Launch the server with the following command and test it with any http request tool.

$ elit serve
$ curl http://0.0.0.0:8000/parse?text=Emory%20NLP%20is%20a%20research%20lab%20in%20Atlanta. | json_pp -json_opt pretty,canonical

You can also post json request to the server with fine-grained control specified in Data Format. We also offer a client which implements ELIT protocol.

from elit.client import Client

def _test_raw_text():
    text = "Emory NLP is a research lab in Atlanta, GA. " \
           "It is founded by Jinho D. Choi in 2014. Dr. Choi is a professor at Emory University."
    nlp = Client('http://0.0.0.0:8000')
    print(nlp.parse(text))


def _test_sents():
    text = ["Emory NLP is a research lab in Atlanta, GA. ",
            "It is founded by Jinho D. Choi in 2014. Dr. Choi is a professor at Emory University."]
    nlp = Client('http://0.0.0.0:8000')
    print(nlp.parse(text))


def _test_tokens():
    tokens = [
        ["Emory", "NLP", "is", "a", "research", "lab", "in", "Atlanta", ",", "GA", "."],
        ["It", "is", "founded", "by", "Jinho", "D.", "Choi", "in", "2014", ".", "Dr.", "Choi", "is", "a", "professor",
         "at", "Emory", "University", "."]
    ]
    nlp = Client('http://0.0.0.0:8000')
    print(nlp.parse(tokens=tokens, models=['ner', 'srl', 'dep'], verbose=True))

GPUs

By default, ELIT tries to use the least occupied GPU so that mostly you don't need to worry about it, ELIT makes the best choice for you. This behavior is very useful when you're using a public server shared across your lab or company with your colleagues.

ELIT also honors the CUDA_VISIBLE_DEVICES used by PyTorch and TensorFlow to limit which devices ELIT can choose from. For example, the following command will only keep the 0th and 1th GPU.

export CUDA_VISIBLE_DEVICES=0,1

Multi-Processing

ELIT server implements 1) request batching and 2) asynchronous coroutine and 3) multi-processing to scale up its computation. The number of processes is default to be 1 and you can adjust it according to your hardware, say 4 processes:

elit serve --workers 4

Models

You can use --parser_model to specify a model identifier for the parsers. It defaults to LEM_POS_NER_DEP_SDP_CON_AMR_ROBERTA_BASE_EN at this point. To use the secondary dependency parser, run the following command:

elit parse --parser_model LEM_POS_NER_DEP_ROBERTA_BASE_EN