wanmok / iterx Goto Github PK

View Code? Open in Web Editor NEW

4.0 3.0 2.0 52.61 MB

This is the repo for the IterX model. The paper was accepted to EACL 2023 and won an outstanding paper award.

Home Page: https://arxiv.org/abs/2210.06600

Jsonnet 4.35% Python 95.65%

iterx's Introduction

Iterative Document-level Information Extraction via Imitation Learning

This repo contains code for the following paper:

Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen, Aaron White, and Benjamin Van Durme. 2023. Iterative Document-level Information Extraction via Imitation Learning. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1858–1874, Dubrovnik, Croatia. Association for Computational Linguistics.

@inproceedings{chen-etal-2023-iterative,
    title = "Iterative Document-level Information Extraction via Imitation Learning",
    author = "Chen, Yunmo  and
      Gantt, William  and
      Gu, Weiwei  and
      Chen, Tongfei  and
      White, Aaron  and
      {Van Durme}, Benjamin",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.136",
    pages = "1858--1874",
    abstract = "We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template{'}s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks {--} 4-ary relation extraction on SciREX and template extraction on MUC-4 {--} as well as a strong baseline on the new BETTER Granular task.",
}

Codebase Release Progress

We are gradually releasing materials related to our paper. The release includes the following:

Beyond our initial release, we are also planning to release the following; however, they are subject to change (in terms of the release date and the content):

Trained checkpoints of IterX models
Trained checkpoints of Span Finder models
Pipeline for running Span Finder and IterX together on new datasets
Migrations of Span Finder and IterX to lightning (if requested by enough users)

Runbook

Environment Setup

Set up Python and Poetry

If you already have a running Python environment, you can skip this section. We tested our codebase on Python 3.11.

Install Python: for Python installation, you can either manage different Python versions through Conda or pyenv. If you were using Conda, we recommend creating a new environment for this project;
Install Poetry using command line by following the Poetry installation;
Make sure that you have added Poetry bin directory to your PATH environment variable.

Set up the IterX Codebase

Clone IterX codebase to your local directory
Create and activate a new virtual environment

If you were using Conda, you can create a new environment by running the following command:
```
conda create -n <your_env_name> python=3.11
conda activate <your_env_name>
```
Enter the project root directory and install the dependencies
```
cd <project_root>
poetry install
```

Run IterX

Before running all IterX related commands, please make sure that you have activated the environment by running the following command from the project root. IterX should be compatible with all AllenNLP commands.

poetry shell

Model Training

You can kick off model training using the same command as ones used in AllenNLP. Here is an example template command:

PYTHONPATH=./src allennlp train \
  --include-package iterx \
  -s <serialization_dir> \
  <path_to_config_file>

If you would like to turn on the BETTER supports, you should also add better to --include-package argument.

Run Evaluation Script

We provide a script for evaluating the model outputs using CEAF-RME metrics. There are two modes of the script, one is to evaluate model outputs and generate corresponding performance scores, the other is to generate prediction comparison results. They share the same CLI arguments and options. You can use --help to get detailed usages.

PYTHONPATH=./src python scripts/ceaf-scorer.py <score_or_compare> \
  --dataset <dataset_name> \
  <path_to_pred_file> \
  <path_to_ref_file>

iterx's People

Contributors

Stargazers

Watchers

Forkers

sidsvash26 wgantt

iterx's Issues

When the code will be released?

Hi,

I am really interested in this paper of using imitation for doc-level extraction and congraduations this work is finally accepted by EACL. Would you release the code then?

allennlp.common.checks.ConfigurationError: key "type" is required at location "model.graph_encoder."

I'm trying to run the model training script for some of my experiments. I installed all the packages in a separate python environment based on the main repo page.

Then I ran:

PYTHONPATH=./src allennlp train \
  --include-package iterx \
  -s new_iterx_dir \
  /data/sid/iterx/resources/training_configs/muc_config.jsonnet

and I get the following error:

allennlp.common.checks.ConfigurationError: key "type" is required at location "model.graph_encoder."

below is the full log:

2023-07-08 08:58:24,308 - INFO - numexpr.utils - Note: NumExpr detected 12 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
2023-07-08 08:58:24,308 - INFO - numexpr.utils - NumExpr defaulting to 8 threads.
2023-07-08 08:58:24,572 - INFO - allennlp.common.params - evaluation = None
2023-07-08 08:58:24,572 - INFO - allennlp.common.params - include_in_archive = None
2023-07-08 08:58:24,572 - INFO - allennlp.common.params - random_seed = 13370
2023-07-08 08:58:24,572 - INFO - allennlp.common.params - numpy_seed = 1337
2023-07-08 08:58:24,572 - INFO - allennlp.common.params - pytorch_seed = 133
2023-07-08 08:58:24,573 - INFO - allennlp.common.checks - Pytorch version: 2.0.1
2023-07-08 08:58:24,574 - INFO - allennlp.common.params - type = default
2023-07-08 08:58:24,574 - INFO - allennlp.common.params - dataset_reader.type = muc
2023-07-08 08:58:24,574 - INFO - allennlp.common.params - dataset_reader.max_instances = None
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.manual_distributed_sharding = False
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.manual_multiprocess_sharding = False
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.definition_file = resources/data/muc/definitions.json
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.token_indexers.tokens.type = pretrained_transformer_mismatched
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.token_indexers.tokens.token_min_padding_length = 0
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.token_indexers.tokens.model_name = t5-large
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.token_indexers.tokens.namespace = tags
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.token_indexers.tokens.max_length = 1024
2023-07-08 08:58:24,575 - INFO - allennlp.common.params - dataset_reader.token_indexers.tokens.tokenizer_kwargs = None
Downloading (…)lve/main/config.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.21k/1.21k [00:00<00:00, 11.2MB/s]
Downloading (…)ve/main/spiece.model: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 792k/792k [00:00<00:00, 12.1MB/s]
Downloading (…)/main/tokenizer.json: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1.39M/1.39M [00:00<00:00, 17.0MB/s]
/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length o
f 512 which will be corrected in Transformers v5.
For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.
- Be aware that you SHOULD NOT rely on t5-large automatically truncating your input to 512 when padding/encoding.
- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.
- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.
  warnings.warn(
2023-07-08 08:58:26,249 - INFO - allennlp.common.params - dataset_reader.is_training = True
2023-07-08 08:58:26,249 - INFO - allennlp.common.params - dataset_reader.skip_docs_without_templates = False
2023-07-08 08:58:26,249 - INFO - allennlp.common.params - dataset_reader.skip_docs_without_spans = True
2023-07-08 08:58:26,249 - INFO - allennlp.common.params - dataset_reader.verbose = False
2023-07-08 08:58:26,265 - INFO - allennlp.common.params - train_data_path = resources/data/muc/preprocessed/tokenized/train.json
2023-07-08 08:58:26,266 - INFO - allennlp.common.params - datasets_for_vocab_creation = None
2023-07-08 08:58:26,266 - INFO - allennlp.common.params - validation_dataset_reader.type = muc
2023-07-08 08:58:26,266 - INFO - allennlp.common.params - validation_dataset_reader.max_instances = None
2023-07-08 08:58:26,266 - INFO - allennlp.common.params - validation_dataset_reader.manual_distributed_sharding = False
2023-07-08 08:58:26,266 - INFO - allennlp.common.params - validation_dataset_reader.manual_multiprocess_sharding = False
2023-07-08 08:58:26,266 - INFO - allennlp.common.params - validation_dataset_reader.definition_file = resources/data/muc/definitions.json
2023-07-08 08:58:26,266 - INFO - allennlp.common.params - validation_dataset_reader.token_indexers.tokens.type = pretrained_transformer_mismatched
2023-07-08 08:58:26,267 - INFO - allennlp.common.params - validation_dataset_reader.token_indexers.tokens.token_min_padding_length = 0
2023-07-08 08:58:26,267 - INFO - allennlp.common.params - validation_dataset_reader.token_indexers.tokens.model_name = t5-large
2023-07-08 08:58:26,267 - INFO - allennlp.common.params - validation_dataset_reader.token_indexers.tokens.namespace = tags
2023-07-08 08:58:26,267 - INFO - allennlp.common.params - validation_dataset_reader.token_indexers.tokens.max_length = 1024
2023-07-08 08:58:26,267 - INFO - allennlp.common.params - validation_dataset_reader.token_indexers.tokens.tokenizer_kwargs = None
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - validation_dataset_reader.is_training = False
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - validation_dataset_reader.skip_docs_without_templates = True
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - validation_dataset_reader.skip_docs_without_spans = True
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - validation_dataset_reader.verbose = False
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - validation_data_path = resources/data/muc/preprocessed/tokenized/dev.json
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - validation_data_loader = None
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - test_data_path = resources/data/muc/preprocessed/tokenized/test.json
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - evaluate_on_test = False
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - batch_weight_key =
2023-07-08 08:58:26,268 - INFO - allennlp.common.params - data_loader.type = multiprocess
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batch_size = None
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.drop_last = False
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.shuffle = False
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batch_sampler.type = bucket
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batch_sampler.batch_size = 1
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batch_sampler.sorting_keys = ['text']
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batch_sampler.padding_noise = 0.1
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batch_sampler.drop_last = False
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batch_sampler.shuffle = True
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.batches_per_epoch = None
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.num_workers = 0
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.max_instances_in_memory = None
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.start_method = fork
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.cuda_device = None
2023-07-08 08:58:26,269 - INFO - allennlp.common.params - data_loader.quiet = False
2023-07-08 08:58:26,270 - INFO - allennlp.common.params - data_loader.collate_fn = <allennlp.data.data_loaders.data_collator.DefaultDataCollator object at 0x7f7065851a90>
loading instances: 0it [00:00, ?it/s]2023-07-08 08:58:26,305 - WARNING - allennlp.data.fields.sequence_label_field - Your label namespace was 'event_types'. We recommend you use a namespace ending
with 'labels' or 'tags', so we don't add UNK and PAD tokens by default to your vocabulary.  See documentation for `non_padded_namespaces` parameter in Vocabulary.
2023-07-08 08:58:26,306 - WARNING - allennlp.data.fields.label_field - Your label namespace was 'slot_types'. We recommend you use a namespace ending with 'labels' or 'tags', so we don't add UNK an
d PAD tokens by default to your vocabulary.  See documentation for `non_padded_namespaces` parameter in Vocabulary.
loading instances: 3995it [00:04, 792.24it/s] 2023-07-08 08:58:30,380 - WARNING - iterx.data.dataset.muc_dataset - Read 1300 documents. Of these, 672 had both templates and spans. 600 had no templa
tes and 628 had no spans.
loading instances: 4032it [00:04, 980.93it/s]
2023-07-08 08:58:30,380 - INFO - allennlp.common.params - data_loader.type = multiprocess
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batch_size = None
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.drop_last = False
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.shuffle = False
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batch_sampler.type = bucket
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batch_sampler.batch_size = 1
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batch_sampler.sorting_keys = ['text']
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batch_sampler.padding_noise = 0.1
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batch_sampler.drop_last = False
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batch_sampler.shuffle = True
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.batches_per_epoch = None
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.num_workers = 0
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.max_instances_in_memory = None
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.start_method = fork
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.cuda_device = None
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.quiet = False
2023-07-08 08:58:30,381 - INFO - allennlp.common.params - data_loader.collate_fn = <allennlp.data.data_loaders.data_collator.DefaultDataCollator object at 0x7f7065851a90>
loading instances: 613it [00:00, 3097.39it/s]2023-07-08 08:58:30,604 - WARNING - iterx.data.dataset.muc_dataset - Read 200 documents. Of these, 111 had both templates and spans. 84 had no templates
 and 89 had no spans.
loading instances: 666it [00:00, 2997.71it/s]
2023-07-08 08:58:30,604 - INFO - allennlp.common.params - data_loader.type = multiprocess
2023-07-08 08:58:30,604 - INFO - allennlp.common.params - data_loader.batch_size = None
2023-07-08 08:58:30,604 - INFO - allennlp.common.params - data_loader.drop_last = False
2023-07-08 08:58:30,604 - INFO - allennlp.common.params - data_loader.shuffle = False
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.batch_sampler.type = bucket
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.batch_sampler.batch_size = 1
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.batch_sampler.sorting_keys = ['text']
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.batch_sampler.padding_noise = 0.1
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.batch_sampler.drop_last = False
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.batch_sampler.shuffle = True
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.batches_per_epoch = None
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.num_workers = 0
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.max_instances_in_memory = None
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.start_method = fork
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.cuda_device = None
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.quiet = False
2023-07-08 08:58:30,605 - INFO - allennlp.common.params - data_loader.collate_fn = <allennlp.data.data_loaders.data_collator.DefaultDataCollator object at 0x7f7065851a90>
loading instances: 409it [00:00, 757.83it/s]2023-07-08 08:58:31,343 - WARNING - iterx.data.dataset.muc_dataset - Read 200 documents. Of these, 123 had both templates and spans. 74 had no templates
and 77 had no spans.
loading instances: 738it [00:00, 1000.58it/s]
2023-07-08 08:58:31,343 - INFO - allennlp.common.params - vocabulary.type = from_files
2023-07-08 08:58:31,343 - INFO - allennlp.common.params - vocabulary.directory = resources/data/muc/vocabulary
2023-07-08 08:58:31,343 - INFO - allennlp.common.params - vocabulary.padding_token = @@PADDING@@
2023-07-08 08:58:31,343 - INFO - allennlp.common.params - vocabulary.oov_token = @@UNKNOWN@@
2023-07-08 08:58:31,344 - INFO - allennlp.data.vocabulary - Loading token dictionary from resources/data/muc/vocabulary.
2023-07-08 08:58:31,344 - INFO - allennlp.common.params - model.type = iterative_template_extraction
2023-07-08 08:58:31,344 - INFO - allennlp.common.params - model.regularizer = None
2023-07-08 08:58:31,344 - INFO - allennlp.common.params - model.definition_file = resources/data/muc/definitions.json
2023-07-08 08:58:31,345 - CRITICAL - root - Uncaught exception
Traceback (most recent call last):
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/params.py", line 211, in pop
    value = self.params.pop(key)
            ^^^^^^^^^^^^^^^^^^^^
KeyError: 'type'

During handling of the above exception, another exception occurred:                                                                                                                         [15/1945]

Traceback (most recent call last):
  File "/home/sidvash/.conda/envs/iterx/bin/allennlp", line 8, in <module>
    sys.exit(run())
             ^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/__main__.py", line 39, in run
    main(prog="allennlp")
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/commands/__init__.py", line 120, in main
    args.func(args)
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/commands/train.py", line 111, in train_model_from_args
    train_model_from_file(
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/commands/train.py", line 177, in train_model_from_file
    return train_model(
           ^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/commands/train.py", line 258, in train_model
    model = _train_worker(
            ^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/commands/train.py", line 494, in _train_worker
    train_loop = TrainModel.from_params(
                 ^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 604, in from_params
    return retyped_subclass.from_params(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 638, in from_params
    return constructor_to_call(**kwargs)  # type: ignore
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/commands/train.py", line 770, in from_partial_objects
    model_ = model.construct(
             ^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/lazy.py", line 82, in construct
    return self.constructor(**contructor_kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/lazy.py", line 66, in constructor_to_use
    return self._constructor.from_params(  # type: ignore[union-attr]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 604, in from_params
    return retyped_subclass.from_params(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 636, in from_params
    kwargs = create_kwargs(constructor_to_inspect, cls, params, **extras)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 206, in create_kwargs
    constructed_arg = pop_and_construct_arg(
                      ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 314, in pop_and_construct_arg
    return construct_arg(class_name, name, popped_params, annotation, default, **extras)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 348, in construct_arg
    result = annotation.from_params(params=popped_params, **subextras)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/from_params.py", line 585, in from_params
    choice = params.pop_choice(
             ^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/params.py", line 314, in pop_choice
    value = self.pop(key, default)
            ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/sidvash/.conda/envs/iterx/lib/python3.11/site-packages/allennlp/common/params.py", line 216, in pop
    raise ConfigurationError(msg)
allennlp.common.checks.ConfigurationError: key "type" is required at location "model.graph_encoder."

It looks like I need to change something in the config file?

Issues with MUC configuration

The current version of resources/training_configs/muc_config.jsonnet does not work out of the box, due to some minor discrepancies between the public code release and the code for which this file was written. I will submit a PR with the relevant changes.

Instructions for Model Inference

There are currently no instructions for running inference on a trained model in the README (only instructions for training and for scoring). We should fix this.

How to train the IterX model? Migrations of Span Finder and IterX to lightning

Traceback (most recent call last):
File "D:\anaconda3\envs\iterx\Lib\json\decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.