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--- |
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datasets: |
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- tner/tweetner7 |
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metrics: |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: tner/bert-base-tweetner7-2020 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: tner/tweetner7 |
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type: tner/tweetner7 |
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args: tner/tweetner7 |
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metrics: |
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- name: F1 (test_2021) |
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type: f1 |
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value: 0.6008989019741707 |
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- name: Precision (test_2021) |
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type: precision |
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value: 0.591443610706686 |
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- name: Recall (test_2021) |
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type: recall |
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value: 0.6106614246068455 |
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- name: Macro F1 (test_2021) |
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type: f1_macro |
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value: 0.5467450408285621 |
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- name: Macro Precision (test_2021) |
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type: precision_macro |
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value: 0.537717358363018 |
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- name: Macro Recall (test_2021) |
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type: recall_macro |
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value: 0.5582367980568581 |
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- name: Entity Span F1 (test_2021) |
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type: f1_entity_span |
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value: 0.7560892328704758 |
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- name: Entity Span Precision (test_2020) |
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type: precision_entity_span |
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value: 0.744313725490196 |
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- name: Entity Span Recall (test_2021) |
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type: recall_entity_span |
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value: 0.7682433213831387 |
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- name: F1 (test_2020) |
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type: f1 |
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value: 0.6087425796006476 |
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- name: Precision (test_2020) |
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type: precision |
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value: 0.6340640809443507 |
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- name: Recall (test_2020) |
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type: recall |
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value: 0.5853658536585366 |
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- name: Macro F1 (test_2020) |
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type: f1_macro |
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value: 0.5648877924450979 |
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- name: Macro Precision (test_2020) |
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type: precision_macro |
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value: 0.5930039411771633 |
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- name: Macro Recall (test_2020) |
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type: recall_macro |
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value: 0.5426595099078766 |
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- name: Entity Span F1 (test_2020) |
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type: f1_entity_span |
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value: 0.7242309767943875 |
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- name: Entity Span Precision (test_2020) |
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type: precision_entity_span |
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value: 0.7543563799887577 |
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- name: Entity Span Recall (test_2020) |
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type: recall_entity_span |
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value: 0.6964193046185781 |
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|
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pipeline_tag: token-classification |
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widget: |
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- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}" |
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example_title: "NER Example 1" |
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--- |
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# tner/bert-base-tweetner7-2020 |
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|
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the |
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[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` split). |
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Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository |
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for more detail). It achieves the following results on the test set of 2021: |
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- F1 (micro): 0.6008989019741707 |
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- Precision (micro): 0.591443610706686 |
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- Recall (micro): 0.6106614246068455 |
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- F1 (macro): 0.5467450408285621 |
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- Precision (macro): 0.537717358363018 |
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- Recall (macro): 0.5582367980568581 |
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The per-entity breakdown of the F1 score on the test set are below: |
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- corporation: 0.4411294619072989 |
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- creative_work: 0.3751552795031057 |
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- event: 0.40279069767441866 |
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- group: 0.5576791808873721 |
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- location: 0.6179921773142112 |
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- person: 0.8051622154507977 |
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- product: 0.6273062730627307 |
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|
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For F1 scores, the confidence interval is obtained by bootstrap as below: |
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- F1 (micro): |
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- 90%: [0.5924664556782363, 0.6106294776916564] |
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- 95%: [0.5905572257793882, 0.6119935888266077] |
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- F1 (macro): |
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- 90%: [0.5924664556782363, 0.6106294776916564] |
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- 95%: [0.5905572257793882, 0.6119935888266077] |
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|
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Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-base-tweetner7-2020/raw/main/eval/metric.json) |
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and [metric file of entity span](https://huggingface.co/tner/bert-base-tweetner7-2020/raw/main/eval/metric_span.json). |
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|
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### Usage |
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This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip |
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```shell |
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pip install tner |
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``` |
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and activate model as below. |
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```python |
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from tner import TransformersNER |
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model = TransformersNER("tner/bert-base-tweetner7-2020") |
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model.predict(["Jacob Collier is a Grammy awarded English artist from London"]) |
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``` |
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It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment. |
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|
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### Training hyperparameters |
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|
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The following hyperparameters were used during training: |
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- dataset: ['tner/tweetner7'] |
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- dataset_split: train_2020 |
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- dataset_name: None |
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- local_dataset: None |
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- model: bert-base-cased |
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- crf: True |
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- max_length: 128 |
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- epoch: 30 |
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- batch_size: 32 |
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- lr: 1e-05 |
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- random_seed: 0 |
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- gradient_accumulation_steps: 1 |
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- weight_decay: 1e-07 |
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- lr_warmup_step_ratio: 0.15 |
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- max_grad_norm: 1 |
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|
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The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/bert-base-tweetner7-2020/raw/main/trainer_config.json). |
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|
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### Reference |
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If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). |
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|
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``` |
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|
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@inproceedings{ushio-camacho-collados-2021-ner, |
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title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", |
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author = "Ushio, Asahi and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", |
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month = apr, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.eacl-demos.7", |
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doi = "10.18653/v1/2021.eacl-demos.7", |
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pages = "53--62", |
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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.", |
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} |
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|
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``` |
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