--- 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-all results: - task: type: token-classification name: Token Classification dataset: name: tner/tweetner7 type: tner/tweetner7 args: tner/tweetner7 metrics: - type: f1 value: 0.6574551220340903 name: F1 (test_2021) - type: precision value: 0.644212629008989 name: Precision (test_2021) - type: recall value: 0.6712534690101758 name: Recall (test_2021) - type: f1_macro value: 0.6124665667529737 name: Macro F1 (test_2021) - type: precision_macro value: 0.6005167968535563 name: Macro Precision (test_2021) - type: recall_macro value: 0.625251837701222 name: Macro Recall (test_2021) - type: f1_entity_span value: 0.7881979839166384 name: Entity Span F1 (test_2021) - type: precision_entity_span value: 0.7722783264898457 name: Entity Span Precision (test_2020) - type: recall_entity_span value: 0.804787787672025 name: Entity Span Recall (test_2021) - type: f1 value: 0.6628787878787878 name: F1 (test_2020) - type: precision value: 0.6924816280384398 name: Precision (test_2020) - type: recall value: 0.6357031655422937 name: Recall (test_2020) - type: f1_macro value: 0.6297223287745568 name: Macro F1 (test_2020) - type: precision_macro value: 0.6618492079232416 name: Macro Precision (test_2020) - type: recall_macro value: 0.601311568050436 name: Macro Recall (test_2020) - type: f1_entity_span value: 0.7642760487144791 name: Entity Span F1 (test_2020) - type: precision_entity_span value: 0.7986425339366516 name: Entity Span Precision (test_2020) - type: recall_entity_span value: 0.7327451997924235 name: Entity Span Recall (test_2020) --- # tner/roberta-large-tweetner7-all This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split). Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository for more detail). It achieves the following results on the test set of 2021: - F1 (micro): 0.6574551220340903 - Precision (micro): 0.644212629008989 - Recall (micro): 0.6712534690101758 - F1 (macro): 0.6124665667529737 - Precision (macro): 0.6005167968535563 - Recall (macro): 0.625251837701222 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.5392156862745098 - creative_work: 0.4760582928521859 - event: 0.4673321234119782 - group: 0.6139798488664987 - location: 0.6707399864222675 - person: 0.8293212669683258 - product: 0.6906187624750498 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.6484148010152769, 0.6672289519134409] - 95%: [0.6470100684797441, 0.6689850350992637] - F1 (macro): - 90%: [0.6484148010152769, 0.6672289519134409] - 95%: [0.6470100684797441, 0.6689850350992637] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-all/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-all/raw/main/eval/metric_span.json). ### Usage This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip. ```shell pip install tner ``` [TweetNER7](https://huggingface.co/datasets/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. ```python 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-all") 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_all - 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](https://huggingface.co/tner/roberta-large-tweetner7-all/raw/main/trainer_config.json). ### 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", } ```