metadata

datasets:
  - tner/tweetner7
metrics:
  - f1
  - precision
  - recall
pipeline_tag: token-classification
widget:
  - text: >-
      Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from
      {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}
    example_title: NER Example 1
base_model: roberta-large
model-index:
  - name: tner/roberta-large-tweetner7-2021
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: tner/tweetner7
          type: tner/tweetner7
          args: tner/tweetner7
        metrics:
          - type: f1
            value: 0.6404513989878424
            name: F1 (test_2021)
          - type: precision
            value: 0.6443872176050568
            name: Precision (test_2021)
          - type: recall
            value: 0.6365633672525439
            name: Recall (test_2021)
          - type: f1_macro
            value: 0.5910583983096561
            name: Macro F1 (test_2021)
          - type: precision_macro
            value: 0.5928837696021392
            name: Macro Precision (test_2021)
          - type: recall_macro
            value: 0.5900571634271187
            name: Macro Recall (test_2021)
          - type: f1_entity_span
            value: 0.7770796974985457
            name: Entity Span F1 (test_2021)
          - type: precision_entity_span
            value: 0.7818096687346365
            name: Entity Span Precision (test_2020)
          - type: recall_entity_span
            value: 0.7724066150109865
            name: Entity Span Recall (test_2021)
          - type: f1
            value: 0.6335644937586686
            name: F1 (test_2020)
          - type: precision
            value: 0.6805721096543504
            name: Precision (test_2020)
          - type: recall
            value: 0.5926310326933056
            name: Recall (test_2020)
          - type: f1_macro
            value: 0.5914520478690088
            name: Macro F1 (test_2020)
          - type: precision_macro
            value: 0.6370623744887871
            name: Macro Precision (test_2020)
          - type: recall_macro
            value: 0.5535477989961968
            name: Macro Recall (test_2020)
          - type: f1_entity_span
            value: 0.7436182019977802
            name: Entity Span F1 (test_2020)
          - type: precision_entity_span
            value: 0.7990459153249851
            name: Entity Span Precision (test_2020)
          - type: recall_entity_span
            value: 0.6953814218993254
            name: Entity Span Recall (test_2020)

tner/roberta-large-tweetner7-2021

This model is a fine-tuned version of roberta-large on the tner/tweetner7 dataset (train_2021 split). Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set of 2021:

F1 (micro): 0.6404513989878424
Precision (micro): 0.6443872176050568
Recall (micro): 0.6365633672525439
F1 (macro): 0.5910583983096561
Precision (macro): 0.5928837696021392
Recall (macro): 0.5900571634271187

The per-entity breakdown of the F1 score on the test set are below:

corporation: 0.5058236272878537
creative_work: 0.43911917098445596
event: 0.46597353497164457
group: 0.6068318821165438
location: 0.6398910823689584
person: 0.8267511177347244
product: 0.6530183727034121

For F1 scores, the confidence interval is obtained by bootstrap as below:

F1 (micro):
- 90%: [0.6310532748860292, 0.6500710194412829]
- 95%: [0.6296658889111393, 0.6521427599284435]
F1 (macro):
- 90%: [0.6310532748860292, 0.6500710194412829]
- 95%: [0.6296658889111393, 0.6521427599284435]

Full evaluation can be found at metric file of NER and metric file of entity span.

Usage

This model can be used through the tner library. Install the library via pip.

pip install tner

TweetNER7 pre-processed tweets where the account name and URLs are converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.

import re
from urlextract import URLExtract
from tner import TransformersNER

extractor = URLExtract()

def format_tweet(tweet):
    # mask web urls
    urls = extractor.find_urls(tweet)
    for url in urls:
        tweet = tweet.replace(url, "{{URL}}")
    # format twitter account
    tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
    return tweet


text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/roberta-large-tweetner7-2021")
model.predict([text_format])

It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.

Training hyperparameters

The following hyperparameters were used during training:

dataset: ['tner/tweetner7']
dataset_split: train_2021
dataset_name: None
local_dataset: None
model: roberta-large
crf: True
max_length: 128
epoch: 30
batch_size: 32
lr: 1e-05
random_seed: 0
gradient_accumulation_steps: 1
weight_decay: 1e-07
lr_warmup_step_ratio: 0.15
max_grad_norm: 1

The full configuration can be found at fine-tuning parameter file.

Reference

If you use the model, please cite T-NER paper and TweetNER7 paper.

T-NER


@inproceedings{ushio-camacho-collados-2021-ner,
    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-demos.7",
    doi = "10.18653/v1/2021.eacl-demos.7",
    pages = "53--62",
    abstract = "Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.",
}

TweetNER7


@inproceedings{ushio-etal-2022-tweet,
    title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
    author = "Ushio, Asahi  and
        Neves, Leonardo  and
        Silva, Vitor  and
        Barbieri, Francesco. and
        Camacho-Collados, Jose",
    booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
    month = nov,
    year = "2022",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}