---
datasets:
- fin
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-fin
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: fin
      type: fin
      args: fin
    metrics:
    - name: F1
      type: f1
      value: 0.6430868167202574
    - name: Precision
      type: precision
      value: 0.6578947368421053
    - name: Recall
      type: recall
      value: 0.6289308176100629
    - name: F1 (macro)
      type: f1_macro
      value: 0.37234464254803534
    - name: Precision (macro)
      type: precision_macro
      value: 0.3758815642868512
    - name: Recall (macro)
      type: recall_macro
      value: 0.3836106023606024
    - name: F1 (entity span)
      type: f1_entity_span
      value: 0.6883116883116883
    - name: Precision (entity span)
      type: precision_entity_span
      value: 0.7043189368770764
    - name: Recall (entity span)
      type: recall_entity_span
      value: 0.6730158730158731

pipeline_tag: token-classification
widget:
- text: "Jacob Collier is a Grammy awarded artist from England."
  example_title: "NER Example 1"
---
# tner/deberta-v3-large-fin

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
[tner/fin](https://huggingface.co/datasets/tner/fin) 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): 0.6430868167202574
- Precision (micro): 0.6578947368421053
- Recall (micro): 0.6289308176100629
- F1 (macro): 0.37234464254803534
- Precision (macro): 0.3758815642868512
- Recall (macro): 0.3836106023606024

The per-entity breakdown of the F1 score on the test set are below:
- LOC: nan
- MISC: nan
- ORG: nan
- PER: nan 

For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro): 
    - 90%: [0.5722111059165758, 0.7112704135498799]
    - 95%: [0.557944362785127, 0.725353903079494] 
- F1 (macro): 
    - 90%: [0.5722111059165758, 0.7112704135498799]
    - 95%: [0.557944362785127, 0.725353903079494] 

Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-fin/raw/main/eval/metric.json) 
and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-fin/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/deberta-v3-large-fin")
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/fin']
 - dataset_split: train
 - dataset_name: None
 - local_dataset: None
 - model: microsoft/deberta-v3-large
 - crf: False
 - max_length: 128
 - epoch: 17
 - 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-fin/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.",
}

```