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---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
model-index:
- name: tner/twitter-roberta-base-2019-90m-tweetner7-2020-2021-concat
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7/test_2021
type: tner/tweetner7/test_2021
args: tner/tweetner7/test_2021
metrics:
- name: F1
type: f1
value: 0.6567966159826227
- name: Precision
type: precision
value: 0.6494460773230839
- name: Recall
type: recall
value: 0.6643154486586494
- name: F1 (macro)
type: f1_macro
value: 0.6099755599654287
- name: Precision (macro)
type: precision_macro
value: 0.602661693428744
- name: Recall (macro)
type: recall_macro
value: 0.6189811354202427
- name: F1 (entity span)
type: f1_entity_span
value: 0.7888869833647745
- name: Precision (entity span)
type: precision_entity_span
value: 0.7800135654533122
- name: Recall (entity span)
type: recall_entity_span
value: 0.7979646120041632
- task:
name: Token Classification
type: token-classification
dataset:
name: tner/tweetner7/test_2020
type: tner/tweetner7/test_2020
args: tner/tweetner7/test_2020
metrics:
- name: F1
type: f1
value: 0.6545553145336225
- name: Precision
type: precision
value: 0.6854060193072118
- name: Recall
type: recall
value: 0.6263622210690192
- name: F1 (macro)
type: f1_macro
value: 0.6121643911579755
- name: Precision (macro)
type: precision_macro
value: 0.6403532739362632
- name: Recall (macro)
type: recall_macro
value: 0.5898647290448411
- name: F1 (entity span)
type: f1_entity_span
value: 0.7643070246813126
- name: Precision (entity span)
type: precision_entity_span
value: 0.8005681818181818
- name: Recall (entity span)
type: recall_entity_span
value: 0.7311883757135443
pipeline_tag: token-classification
widget:
- text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}"
example_title: "NER Example 1"
---
# tner/twitter-roberta-base-2019-90m-tweetner7-2020-2021-concat
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on the
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_all` split).
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 of 2021:
- F1 (micro): 0.6567966159826227
- Precision (micro): 0.6494460773230839
- Recall (micro): 0.6643154486586494
- F1 (macro): 0.6099755599654287
- Precision (macro): 0.602661693428744
- Recall (macro): 0.6189811354202427
The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.5087071240105541
- creative_work: 0.4729907773386035
- event: 0.48405253283302063
- group: 0.6147885050048434
- location: 0.679419525065963
- person: 0.83927591881514
- product: 0.6705945366898768
For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro):
- 90%: [0.648368394653773, 0.6664006471768674]
- 95%: [0.646545111092117, 0.6680503208004025]
- F1 (macro):
- 90%: [0.648368394653773, 0.6664006471768674]
- 95%: [0.646545111092117, 0.6680503208004025]
Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2020-2021-concat/raw/main/eval/metric.json)
and [metric file of entity span](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2020-2021-concat/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/twitter-roberta-base-2019-90m-tweetner7-2020-2021-concat")
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/tweetner7']
- dataset_split: train_all
- dataset_name: None
- local_dataset: None
- model: cardiffnlp/twitter-roberta-base-2019-90m
- crf: True
- max_length: 128
- epoch: 30
- batch_size: 32
- lr: 1e-05
- random_seed: 0
- gradient_accumulation_steps: 1
- weight_decay: 1e-07
- lr_warmup_step_ratio: 0.15
- max_grad_norm: 1
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2020-2021-concat/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.",
}
```