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---
datasets:
- wnut2017
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-wnut2017
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut2017
type: wnut2017
args: wnut2017
metrics:
- name: F1
type: f1
value: 0.5047353760445682
- name: Precision
type: precision
value: 0.63268156424581
- name: Recall
type: recall
value: 0.4198331788693234
- name: F1 (macro)
type: f1_macro
value: 0.4165125500830091
- name: Precision (macro)
type: precision_macro
value: 0.5356144444686111
- name: Recall (macro)
type: recall_macro
value: 0.3573954549633822
- name: F1 (entity span)
type: f1_entity_span
value: 0.6249999999999999
- name: Precision (entity span)
type: precision_entity_span
value: 0.7962697274031564
- name: Recall (entity span)
type: recall_entity_span
value: 0.5143651529193698
pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
example_title: "NER Example 1"
---
# tner/deberta-v3-large-wnut2017
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the
[tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) 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): 0.5047353760445682
- Precision (micro): 0.63268156424581
- Recall (micro): 0.4198331788693234
- F1 (macro): 0.4165125500830091
- Precision (macro): 0.5356144444686111
- Recall (macro): 0.3573954549633822
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.25477707006369427
- group: 0.34309623430962344
- location: 0.6187050359712232
- person: 0.6721763085399448
- product: 0.18579234972677597
- work_of_art: 0.42452830188679247
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.4752384997212858, 0.5329114690850492]
- 95%: [0.46929053844001617, 0.537282841423422]
- F1 (macro):
- 90%: [0.4752384997212858, 0.5329114690850492]
- 95%: [0.46929053844001617, 0.537282841423422]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric_span.json).
### Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/wnut2017']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: False
- max_length: 128
- epoch: 15
- batch_size: 16
- lr: 1e-05
- random_seed: 42
- gradient_accumulation_steps: 4
- weight_decay: 1e-07
- 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/deberta-v3-large-wnut2017/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.",
}
```
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