metadata
datasets:
- bc5cdr
metrics:
- f1
- precision
- recall
model-index:
- name: tner/deberta-v3-large-bc5cdr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: bc5cdr
type: bc5cdr
args: bc5cdr
metrics:
- name: F1
type: f1
value: 0.8902493653874869
- name: Precision
type: precision
value: 0.8697724178175452
- name: Recall
type: recall
value: 0.9117137322866755
- name: F1 (macro)
type: f1_macro
value: 0.8863403908610603
- name: Precision (macro)
type: precision_macro
value: 0.8657302393432342
- name: Recall (macro)
type: recall_macro
value: 0.9080747413030301
- name: F1 (entity span)
type: f1_entity_span
value: 0.8929371360310587
- name: Precision (entity span)
type: precision_entity_span
value: 0.8723983660766388
- name: Recall (entity span)
type: recall_entity_span
value: 0.9144663064532572
pipeline_tag: token-classification
widget:
- text: Jacob Collier is a Grammy awarded artist from England.
example_title: NER Example 1
tner/deberta-v3-large-bc5cdr
This model is a fine-tuned version of microsoft/deberta-v3-large on the tner/bc5cdr dataset. Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set:
- F1 (micro): 0.8902493653874869
- Precision (micro): 0.8697724178175452
- Recall (micro): 0.9117137322866755
- F1 (macro): 0.8863403908610603
- Precision (macro): 0.8657302393432342
- Recall (macro): 0.9080747413030301
The per-entity breakdown of the F1 score on the test set are below:
- chemical: 0.9298502009499452
- disease: 0.8428305807721753
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.885162383660078, 0.8951239957151518]
- 95%: [0.8838793313408008, 0.8959517574197015]
- F1 (macro):
- 90%: [0.885162383660078, 0.8951239957151518]
- 95%: [0.8838793313408008, 0.8959517574197015]
Full evaluation can be found at metric file of NER and metric file of entity span.
Training hyperparameters
The following hyperparameters were used during training:
- dataset: ['tner/bc5cdr']
- dataset_split: train
- dataset_name: None
- local_dataset: None
- model: microsoft/deberta-v3-large
- crf: True
- 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: None
The full configuration can be found at fine-tuning parameter file.
Reference
If you use any resource from T-NER, please consider to cite our paper.
@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.",
}