| | --- |
| | datasets: |
| | - tner/ontonotes5 |
| | metrics: |
| | - f1 |
| | - precision |
| | - recall |
| | model-index: |
| | - name: tner/roberta-large-ontonotes5 |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: tner/ontonotes5 |
| | type: tner/ontonotes5 |
| | args: tner/ontonotes5 |
| | metrics: |
| | - name: F1 |
| | type: f1 |
| | value: 0.908632361399938 |
| | - name: Precision |
| | type: precision |
| | value: 0.905148095909732 |
| | - name: Recall |
| | type: recall |
| | value: 0.9121435551212579 |
| | - name: F1 (macro) |
| | type: f1_macro |
| | value: 0.8265477704565624 |
| | - name: Precision (macro) |
| | type: precision_macro |
| | value: 0.8170668848546687 |
| | - name: Recall (macro) |
| | type: recall_macro |
| | value: 0.8387672780349001 |
| | - name: F1 (entity span) |
| | type: f1_entity_span |
| | value: 0.9284544931640193 |
| | - name: Precision (entity span) |
| | type: precision_entity_span |
| | value: 0.9248942172073342 |
| | - name: Recall (entity span) |
| | type: recall_entity_span |
| | value: 0.9320422848005685 |
| |
|
| | pipeline_tag: token-classification |
| | widget: |
| | - text: "Jacob Collier is a Grammy awarded artist from England." |
| | example_title: "NER Example 1" |
| | --- |
| | # tner/roberta-large-ontonotes5 |
| |
|
| | This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the |
| | [tner/ontonotes5](https://huggingface.co/datasets/tner/ontonotes5) dataset. |
| | 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: |
| | - F1 (micro): 0.908632361399938 |
| | - Precision (micro): 0.905148095909732 |
| | - Recall (micro): 0.9121435551212579 |
| | - F1 (macro): 0.8265477704565624 |
| | - Precision (macro): 0.8170668848546687 |
| | - Recall (macro): 0.8387672780349001 |
| |
|
| | The per-entity breakdown of the F1 score on the test set are below: |
| | - cardinal_number: 0.8605277329025309 |
| | - date: 0.872996300863132 |
| | - event: 0.7424242424242424 |
| | - facility: 0.7732342007434945 |
| | - geopolitical_area: 0.9687148323205043 |
| | - group: 0.9470588235294117 |
| | - language: 0.7499999999999999 |
| | - law: 0.6666666666666666 |
| | - location: 0.7593582887700535 |
| | - money: 0.901098901098901 |
| | - ordinal_number: 0.85785536159601 |
| | - organization: 0.9227360841872057 |
| | - percent: 0.9171428571428571 |
| | - person: 0.9556004036326943 |
| | - product: 0.7857142857142858 |
| | - quantity: 0.7945205479452055 |
| | - time: 0.6870588235294116 |
| | - work_of_art: 0.7151515151515151 |
| | |
| | For F1 scores, the confidence interval is obtained by bootstrap as below: |
| | - F1 (micro): |
| | - 90%: [0.9039454247544766, 0.9128956119702822] |
| | - 95%: [0.9030263216115454, 0.9138350859566045] |
| | - F1 (macro): |
| | - 90%: [0.9039454247544766, 0.9128956119702822] |
| | - 95%: [0.9030263216115454, 0.9138350859566045] |
| | |
| | Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-ontonotes5/raw/main/eval/metric.json) |
| | and [metric file of entity span](https://huggingface.co/tner/roberta-large-ontonotes5/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-ontonotes5") |
| | 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/ontonotes5'] |
| | - dataset_split: train |
| | - dataset_name: None |
| | - local_dataset: None |
| | - model: roberta-large |
| | - crf: True |
| | - max_length: 128 |
| | - epoch: 15 |
| | - batch_size: 64 |
| | - lr: 1e-05 |
| | - random_seed: 42 |
| | - gradient_accumulation_steps: 1 |
| | - weight_decay: None |
| | - lr_warmup_step_ratio: 0.1 |
| | - max_grad_norm: 10.0 |
| | |
| | The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/roberta-large-ontonotes5/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.", |
| | } |
| | |
| | ``` |
| | |
