--- datasets: - tner/tweetner7 metrics: - f1 - precision - recall model-index: - name: tner/roberta-large-tweetner7-random results: - task: name: Token Classification type: token-classification dataset: name: tner/tweetner7 type: tner/tweetner7 args: tner/tweetner7 metrics: - name: F1 (test_2021) type: f1 value: 0.6632769652650823 - name: Precision (test_2021) type: precision value: 0.6554878048780488 - name: Recall (test_2021) type: recall value: 0.6712534690101758 - name: Macro F1 (test_2021) type: f1_macro value: 0.6096477771855761 - name: Macro Precision (test_2021) type: precision_macro value: 0.6042443991246051 - name: Macro Recall (test_2021) type: recall_macro value: 0.6191008735553379 - name: Entity Span F1 (test_2021) type: f1_entity_span value: 0.7900359938296291 - name: Entity Span Precision (test_2020) type: precision_entity_span value: 0.780713640469738 - name: Entity Span Recall (test_2021) type: recall_entity_span value: 0.7995836706372152 - name: F1 (test_2020) type: f1 value: 0.6439847577572129 - name: Precision (test_2020) type: precision value: 0.6771608471665712 - name: Recall (test_2020) type: recall value: 0.6139076284379865 - name: Macro F1 (test_2020) type: f1_macro value: 0.6008744778169367 - name: Macro Precision (test_2020) type: precision_macro value: 0.6358142893696356 - name: Macro Recall (test_2020) type: recall_macro value: 0.5742193301311931 - name: Entity Span F1 (test_2020) type: f1_entity_span value: 0.7552409474543968 - name: Entity Span Precision (test_2020) type: precision_entity_span value: 0.7943871706758304 - name: Entity Span Recall (test_2020) type: recall_entity_span value: 0.7197716658017644 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" --- # tner/roberta-large-tweetner7-random 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_random` 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): 0.6632769652650823 - Precision (micro): 0.6554878048780488 - Recall (micro): 0.6712534690101758 - F1 (macro): 0.6096477771855761 - Precision (macro): 0.6042443991246051 - Recall (macro): 0.6191008735553379 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.5224148236700539 - creative_work: 0.45186640471512773 - event: 0.4894837476099427 - group: 0.6327722432153899 - location: 0.6692258477287268 - person: 0.838405036726128 - product: 0.6633663366336633 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.6546824558783396, 0.6722355436189195] - 95%: [0.6527609558375069, 0.6741666937877734] - F1 (macro): - 90%: [0.6546824558783396, 0.6722355436189195] - 95%: [0.6527609558375069, 0.6741666937877734] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-random/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-random/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-random") 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_random - 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-random/raw/main/trainer_config.json). ### Reference If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @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.", } ```