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--- |
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datasets: |
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- tner/tweetner7 |
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metrics: |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: tner/twitter-roberta-base-2019-90m-tweetner7-2021 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: tner/tweetner7 |
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type: tner/tweetner7 |
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args: tner/tweetner7 |
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metrics: |
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- name: F1 (test_2021) |
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type: f1 |
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value: 0.6323179293083451 |
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- name: Precision (test_2021) |
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type: precision |
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value: 0.669727143411354 |
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- name: Recall (test_2021) |
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type: recall |
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value: 0.5988667900092507 |
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- name: Macro F1 (test_2021) |
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type: f1_macro |
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value: 0.5671647388688191 |
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- name: Macro Precision (test_2021) |
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type: precision_macro |
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value: 0.6049623231784063 |
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- name: Macro Recall (test_2021) |
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type: recall_macro |
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value: 0.5439217682783225 |
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- name: Entity Span F1 (test_2021) |
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type: f1_entity_span |
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value: 0.756898656898657 |
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- name: Entity Span Precision (test_2020) |
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type: precision_entity_span |
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value: 0.801629380576749 |
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- name: Entity Span Recall (test_2021) |
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type: recall_entity_span |
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value: 0.716896033306349 |
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- name: F1 (test_2020) |
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type: f1 |
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value: 0.6190614981055086 |
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- name: Precision (test_2020) |
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type: precision |
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value: 0.7061170212765957 |
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- name: Recall (test_2020) |
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type: recall |
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value: 0.5511157239231966 |
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- name: Macro F1 (test_2020) |
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type: f1_macro |
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value: 0.560944714651875 |
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- name: Macro Precision (test_2020) |
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type: precision_macro |
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value: 0.6527607933480432 |
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- name: Macro Recall (test_2020) |
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type: recall_macro |
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value: 0.5048039912609852 |
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- name: Entity Span F1 (test_2020) |
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type: f1_entity_span |
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value: 0.7303993004954824 |
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- name: Entity Span Precision (test_2020) |
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type: precision_entity_span |
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value: 0.8331117021276596 |
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- name: Entity Span Recall (test_2020) |
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type: recall_entity_span |
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value: 0.6502335236118318 |
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pipeline_tag: token-classification |
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widget: |
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- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}" |
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example_title: "NER Example 1" |
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--- |
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# tner/twitter-roberta-base-2019-90m-tweetner7-2021 |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on the |
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[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2021` split). |
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Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository |
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for more detail). It achieves the following results on the test set of 2021: |
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- F1 (micro): 0.6323179293083451 |
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- Precision (micro): 0.669727143411354 |
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- Recall (micro): 0.5988667900092507 |
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- F1 (macro): 0.5671647388688191 |
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- Precision (macro): 0.6049623231784063 |
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- Recall (macro): 0.5439217682783225 |
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The per-entity breakdown of the F1 score on the test set are below: |
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- corporation: 0.4673109721432633 |
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- creative_work: 0.33124018838304553 |
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- event: 0.4597107438016529 |
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- group: 0.5760869565217391 |
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- location: 0.6441717791411042 |
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- person: 0.8321060382916053 |
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- product: 0.6595264937993236 |
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|
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For F1 scores, the confidence interval is obtained by bootstrap as below: |
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- F1 (micro): |
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- 90%: [0.6233520895930195, 0.6418683671749849] |
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- 95%: [0.6212129239683746, 0.6433834953337656] |
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- F1 (macro): |
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- 90%: [0.6233520895930195, 0.6418683671749849] |
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- 95%: [0.6212129239683746, 0.6433834953337656] |
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Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2021/raw/main/eval/metric.json) |
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and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2021/raw/main/eval/metric_span.json). |
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### Usage |
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This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip. |
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```shell |
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pip install tner |
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``` |
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[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are |
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converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below. |
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|
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```python |
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import re |
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from urlextract import URLExtract |
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from tner import TransformersNER |
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extractor = URLExtract() |
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def format_tweet(tweet): |
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# mask web urls |
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urls = extractor.find_urls(tweet) |
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for url in urls: |
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tweet = tweet.replace(url, "{{URL}}") |
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# format twitter account |
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tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet) |
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return tweet |
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text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek" |
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text_format = format_tweet(text) |
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model = TransformersNER("tner/twitter-roberta-base-2019-90m-tweetner7-2021") |
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model.predict([text_format]) |
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``` |
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It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. |
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|
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- dataset: ['tner/tweetner7'] |
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- dataset_split: train_2021 |
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- dataset_name: None |
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- local_dataset: None |
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- model: cardiffnlp/twitter-roberta-base-2019-90m |
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- crf: True |
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- max_length: 128 |
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- epoch: 30 |
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- batch_size: 32 |
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- lr: 0.0001 |
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- random_seed: 0 |
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- gradient_accumulation_steps: 1 |
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- weight_decay: 1e-07 |
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- lr_warmup_step_ratio: 0.3 |
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- max_grad_norm: 1 |
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The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2021/raw/main/trainer_config.json). |
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### Reference |
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If you use the model, please cite T-NER paper and TweetNER7 paper. |
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- T-NER |
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``` |
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|
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@inproceedings{ushio-camacho-collados-2021-ner, |
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title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", |
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author = "Ushio, Asahi and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", |
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month = apr, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.eacl-demos.7", |
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doi = "10.18653/v1/2021.eacl-demos.7", |
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pages = "53--62", |
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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.", |
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} |
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|
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``` |
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- TweetNER7 |
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``` |
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|
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@inproceedings{ushio-etal-2022-tweet, |
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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", |
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author = "Ushio, Asahi and |
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Neves, Leonardo and |
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Silva, Vitor and |
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Barbieri, Francesco. and |
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Camacho-Collados, Jose", |
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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", |
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month = nov, |
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year = "2022", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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} |
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|
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``` |
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