--- datasets: - tner/tweetner7 metrics: - f1 - precision - recall model-index: - name: tner/bert-base-tweetner7-2020-2021-concat results: - task: name: Token Classification type: token-classification dataset: name: tner/tweetner7/test_2021 type: tner/tweetner7/test_2021 args: tner/tweetner7/test_2021 metrics: - name: F1 type: f1 value: 0.6230258640421148 - name: Precision type: precision value: 0.6166742183960127 - name: Recall type: recall value: 0.6295097132284921 - name: F1 (macro) type: f1_macro value: 0.5758556427048315 - name: Precision (macro) type: precision_macro value: 0.5715554663683273 - name: Recall (macro) type: recall_macro value: 0.5821234872899773 - name: F1 (entity span) type: f1_entity_span value: 0.7661839619941617 - name: Precision (entity span) type: precision_entity_span value: 0.7584995466908432 - name: Recall (entity span) type: recall_entity_span value: 0.7740256736440384 - task: name: Token Classification type: token-classification dataset: name: tner/tweetner7/test_2020 type: tner/tweetner7/test_2020 args: tner/tweetner7/test_2020 metrics: - name: F1 type: f1 value: 0.6210070384407147 - name: Precision type: precision value: 0.6491228070175439 - name: Recall type: recall value: 0.5952257394914374 - name: F1 (macro) type: f1_macro value: 0.577436139660066 - name: Precision (macro) type: precision_macro value: 0.6119340101835135 - name: Recall (macro) type: recall_macro value: 0.549500601374034 - name: F1 (entity span) type: f1_entity_span value: 0.7298321602598808 - name: Precision (entity span) type: precision_entity_span value: 0.7628749292586304 - name: Recall (entity span) type: recall_entity_span value: 0.6995329527763363 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/bert-base-tweetner7-2020-2021-concat This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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): 0.6230258640421148 - Precision (micro): 0.6166742183960127 - Recall (micro): 0.6295097132284921 - F1 (macro): 0.5758556427048315 - Precision (macro): 0.5715554663683273 - Recall (macro): 0.5821234872899773 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.5141176470588235 - creative_work: 0.3886075949367089 - event: 0.4580617122990004 - group: 0.5660613650594865 - location: 0.6264564770390679 - person: 0.8196536144578314 - product: 0.6580310880829014 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.6139925448708724, 0.632549139769655] - 95%: [0.612303125388328, 0.6336744975616968] - F1 (macro): - 90%: [0.6139925448708724, 0.632549139769655] - 95%: [0.612303125388328, 0.6336744975616968] Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-base-tweetner7-2020-2021-concat/raw/main/eval/metric.json) and [metric file of entity span](https://huggingface.co/tner/bert-base-tweetner7-2020-2021-concat/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/bert-base-tweetner7-2020-2021-concat") 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_all - dataset_name: None - local_dataset: None - model: bert-base-cased - crf: True - max_length: 128 - epoch: 30 - batch_size: 32 - lr: 0.0001 - random_seed: 0 - gradient_accumulation_steps: 1 - weight_decay: 1e-07 - lr_warmup_step_ratio: 0.3 - max_grad_norm: 1 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bert-base-tweetner7-2020-2021-concat/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.", } ```