--- 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 {{@Herbie Hancock@}} via {{USERNAME}} 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 ``` and activate model as below. ```python from tner import TransformersNER model = TransformersNER("tner/roberta-large-tweetner7-random") model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) ``` 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.", } ```