README.md

---
datasets:
- tner/tweetner7
metrics:
- f1
- precision
- recall
pipeline_tag: token-classification
widget:
- text: 'Get the all-analog Classic Vinyl Edition of `Takin'' Off` Album from {@herbiehancock@}
    via {@bluenoterecords@} link below: {{URL}}'
  example_title: NER Example 1
base_model: bert-base-cased
model-index:
- name: tner/bert-base-tweetner7-2020
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: tner/tweetner7
      type: tner/tweetner7
      args: tner/tweetner7
    metrics:
    - type: f1
      value: 0.6008989019741707
      name: F1 (test_2021)
    - type: precision
      value: 0.591443610706686
      name: Precision (test_2021)
    - type: recall
      value: 0.6106614246068455
      name: Recall (test_2021)
    - type: f1_macro
      value: 0.5467450408285621
      name: Macro F1 (test_2021)
    - type: precision_macro
      value: 0.537717358363018
      name: Macro Precision (test_2021)
    - type: recall_macro
      value: 0.5582367980568581
      name: Macro Recall (test_2021)
    - type: f1_entity_span
      value: 0.7560892328704758
      name: Entity Span F1 (test_2021)
    - type: precision_entity_span
      value: 0.744313725490196
      name: Entity Span Precision (test_2020)
    - type: recall_entity_span
      value: 0.7682433213831387
      name: Entity Span Recall (test_2021)
    - type: f1
      value: 0.6087425796006476
      name: F1 (test_2020)
    - type: precision
      value: 0.6340640809443507
      name: Precision (test_2020)
    - type: recall
      value: 0.5853658536585366
      name: Recall (test_2020)
    - type: f1_macro
      value: 0.5648877924450979
      name: Macro F1 (test_2020)
    - type: precision_macro
      value: 0.5930039411771633
      name: Macro Precision (test_2020)
    - type: recall_macro
      value: 0.5426595099078766
      name: Macro Recall (test_2020)
    - type: f1_entity_span
      value: 0.7242309767943875
      name: Entity Span F1 (test_2020)
    - type: precision_entity_span
      value: 0.7543563799887577
      name: Entity Span Precision (test_2020)
    - type: recall_entity_span
      value: 0.6964193046185781
      name: Entity Span Recall (test_2020)
---
# tner/bert-base-tweetner7-2020

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the 
[tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train_2020` 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): 0.6008989019741707
- Precision (micro): 0.591443610706686
- Recall (micro): 0.6106614246068455
- F1 (macro): 0.5467450408285621
- Precision (macro): 0.537717358363018
- Recall (macro): 0.5582367980568581



The per-entity breakdown of the F1 score on the test set are below:
- corporation: 0.4411294619072989
- creative_work: 0.3751552795031057
- event: 0.40279069767441866
- group: 0.5576791808873721
- location: 0.6179921773142112
- person: 0.8051622154507977
- product: 0.6273062730627307 

For F1 scores, the confidence interval is obtained by bootstrap as below:
- F1 (micro): 
    - 90%: [0.5924664556782363, 0.6106294776916564]
    - 95%: [0.5905572257793882, 0.6119935888266077] 
- F1 (macro): 
    - 90%: [0.5924664556782363, 0.6106294776916564]
    - 95%: [0.5905572257793882, 0.6119935888266077] 

Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/bert-base-tweetner7-2020/raw/main/eval/metric.json) 
and [metric file of entity span](https://huggingface.co/tner/bert-base-tweetner7-2020/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
```
[TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are 
converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.  

```python
import re
from urlextract import URLExtract
from tner import TransformersNER

extractor = URLExtract()

def format_tweet(tweet):
    # mask web urls
    urls = extractor.find_urls(tweet)
    for url in urls:
        tweet = tweet.replace(url, "{{URL}}")
    # format twitter account
    tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
    return tweet


text = "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"
text_format = format_tweet(text)
model = TransformersNER("tner/bert-base-tweetner7-2020")
model.predict([text_format])
```
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_2020
 - dataset_name: None
 - local_dataset: None
 - model: bert-base-cased
 - 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/bert-base-tweetner7-2020/raw/main/trainer_config.json).

### Reference
If you use the model, please cite T-NER paper and TweetNER7 paper.
- T-NER
```

@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.",
}

```
- TweetNER7
```

@inproceedings{ushio-etal-2022-tweet,
    title = "{N}amed {E}ntity {R}ecognition in {T}witter: {A} {D}ataset and {A}nalysis on {S}hort-{T}erm {T}emporal {S}hifts",
    author = "Ushio, Asahi  and
        Neves, Leonardo  and
        Silva, Vitor  and
        Barbieri, Francesco. and
        Camacho-Collados, Jose",
    booktitle = "The 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
    month = nov,
    year = "2022",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}

```