Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
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---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TweetNER7
---
# Dataset Card for "tner/tweetner7"
## Dataset Description
- **Repository:** [https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper)
- **Paper:** TBA
- **Dataset:** TweetNER7
- **Domain:** Twitter
- **Number of Entity:** 7
### Dataset Summary
This is the official repository of TweetNER7 ("Named Entity Recognition in Twitter:
A Dataset and Analysis on Short-Term Temporal Shifts, AACL main conference 2022"), an NER dataset on Twitter with 7 entity labels. Each instance of TweetNER7 comes with a timestamp which distributes from September 2019 to August 2021.
The tweet collection used in TweetNER7 is same as what used in [TweetTopic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi).
The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
- Entity Types: `corperation`, `creative_work`, `event`, `group`, `location`, `product`, `person`
### Preprocessing
We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
For verified usernames, we replace its display name (or account name) with symbols `{@}`.
For example, a tweet
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below:
http://bluenote.lnk.to/AlbumOfTheWeek
```
is transformed into the following text.
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}
```
A simple function to format tweet follows below.
```python
import re
from urlextract import URLExtract
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
target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
```
We ask annotators to ignore those special tokens but label the verified users' mentions.
### Data Split
| split | number of instances | description |
|:------------------|------:|------:|
| train_2020 | 4616 | training dataset from September 2019 to August 2020 |
| train_2021 | 2495 | training dataset from September 2020 to August 2021 |
| train_all | 7111 | combined training dataset of `train_2020` and `train_2021` |
| validation_2020 | 576 | validation dataset from September 2019 to August 2020 |
| validation_2021 | 310 | validation dataset from September 2020 to August 2021 |
| test_2020 | 576 | test dataset from September 2019 to August 2020 |
| test_2021 | 2807 | test dataset from September 2020 to August 2021 |
| train_random | 4616 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
| validation_random | 576 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
| extra_2020 | 87880 | extra tweet without annotations from September 2019 to August 2020 |
| extra_2021 | 93594 | extra tweet without annotations from September 2020 to August 2021 |
For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tokens': ['Morning', '5km', 'run', 'with', '{{USERNAME}}', 'for', 'breast', 'cancer', 'awareness', '#', 'pinkoctober', '#', 'breastcancerawareness', '#', 'zalorafit', '#', 'zalorafitxbnwrc', '@', 'The', 'Central', 'Park', ',', 'Desa', 'Parkcity', '{{URL}}'],
'tags': [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 2, 14, 2, 14, 14, 14, 14, 14, 14, 4, 11, 11, 11, 11, 14],
'id': '1183344337016381440',
'date': '2019-10-13'
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweetner7/raw/main/dataset/label.json).
```python
{
"B-corporation": 0,
"B-creative_work": 1,
"B-event": 2,
"B-group": 3,
"B-location": 4,
"B-person": 5,
"B-product": 6,
"I-corporation": 7,
"I-creative_work": 8,
"I-event": 9,
"I-group": 10,
"I-location": 11,
"I-person": 12,
"I-product": 13,
"O": 14
}
```
## Models
| Model (link) | Data | Language Model | Micro F1 (2021) | Macro F1 (2021) | F1 (2021)/corporation | F1 (2021)/creative_work | F1 (2021)/event | F1 (2021)/group | F1 (2021)/location | F1 (2021)/person | F1 (2021)/product | Micro F1 (2020) | Macro F1 (2020) | F1 (2020)/corporation | F1 (2020)/creative_work | F1 (2020)/event | F1 (2020)/group | F1 (2020)/location | F1 (2020)/person | F1 (2020)/product | Entity-Span F1 (2021) | Entity-Span F1 (2020) |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------------:|------------------------:|
| [`tner/roberta-large-tweetner7-all`](https://huggingface.co/tner/roberta-large-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 65.75 | 61.25 | 53.92 | 47.61 | 46.73 | 61.4 | 67.07 | 82.93 | 69.06 | 66.29 | 62.97 | 61.84 | 51.59 | 50.29 | 55.99 | 69.23 | 82.01 | 69.86 | 78.82 | 76.43 |
| [`tner/roberta-base-tweetner7-all`](https://huggingface.co/tner/roberta-base-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 65.16 | 60.81 | 51.74 | 46.64 | 46.73 | 60.71 | 68.33 | 83.77 | 67.77 | 65.32 | 61.66 | 61.94 | 48.94 | 45.14 | 56.58 | 68.94 | 82.75 | 67.33 | 78.93 | 75.23 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-all`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 65.68 | 61 | 50.87 | 47.3 | 48.41 | 61.48 | 67.94 | 83.93 | 67.06 | 65.46 | 61.22 | 56.85 | 52.15 | 46.68 | 56.68 | 65.1 | 84.55 | 66.5 | 78.89 | 76.43 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-all`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 65.26 | 60.7 | 51.53 | 47.6 | 46.69 | 60.93 | 66.89 | 83.87 | 67.38 | 65.44 | 61.39 | 56.76 | 55.06 | 46.24 | 55.52 | 64.26 | 84.87 | 67 | 78.68 | 75.87 |
| [`tner/bertweet-large-tweetner7-all`](https://huggingface.co/tner/bertweet-large-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 66.46 | 61.87 | 54.5 | 47.36 | 49.15 | 62.38 | 67.55 | 84.15 | 68.02 | 66.76 | 63.08 | 58.89 | 55.24 | 48.89 | 59.85 | 66.67 | 83.49 | 68.51 | 79.53 | 77.59 |
| [`tner/bertweet-base-tweetner7-all`](https://huggingface.co/tner/bertweet-base-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 65.36 | 60.52 | 52.51 | 46.54 | 48.06 | 60.33 | 65.67 | 84.08 | 66.46 | 65.74 | 61.61 | 57.22 | 54.1 | 48.55 | 57.35 | 64.57 | 84.16 | 65.36 | 78.99 | 76.91 |
| [`tner/bert-large-tweetner7-all`](https://huggingface.co/tner/bert-large-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 63.58 | 59 | 50.13 | 40.16 | 47 | 59.74 | 67.2 | 81.86 | 66.91 | 62.49 | 58.63 | 55.56 | 47.65 | 43.08 | 54.88 | 63.9 | 80.31 | 65.04 | 77.21 | 73.58 |
| [`tner/bert-base-tweetner7-all`](https://huggingface.co/tner/bert-base-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 62.3 | 57.59 | 51.41 | 38.86 | 45.81 | 56.61 | 62.65 | 81.97 | 65.8 | 62.1 | 57.74 | 56.55 | 41.52 | 45.04 | 54.23 | 60.53 | 81.86 | 64.49 | 76.62 | 72.98 |
| [`tner/roberta-large-tweetner7-continuous`](https://huggingface.co/tner/roberta-large-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 66.02 | 60.9 | 53.15 | 44.42 | 48.79 | 61.15 | 67.41 | 84.72 | 66.63 | 66.26 | 62.4 | 57.75 | 54.14 | 48.48 | 57.52 | 67.69 | 83.33 | 67.84 | 79.14 | 76.44 |
| [`tner/roberta-base-tweetner7-continuous`](https://huggingface.co/tner/roberta-base-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 65.47 | 60.01 | 50.97 | 41.68 | 46.75 | 61.52 | 67.98 | 84.49 | 66.67 | 65.15 | 60.82 | 58.05 | 49.85 | 44.74 | 56.05 | 67.08 | 82.63 | 67.33 | 78.1 | 75.05 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-continuous`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 65.87 | 61.07 | 51.66 | 48.01 | 48.47 | 60.42 | 68.36 | 84.59 | 66.01 | 64.76 | 60.58 | 56.19 | 54.97 | 44.67 | 53.17 | 63.53 | 83.64 | 67.88 | 78.44 | 75.53 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-continuous`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 65.51 | 60.57 | 53.56 | 45.3 | 46.92 | 61.07 | 66.28 | 84.33 | 66.49 | 65.29 | 61.28 | 59.26 | 55.59 | 43.84 | 54.38 | 64.14 | 84.08 | 67.68 | 78.03 | 75.88 |
| [`tner/bertweet-large-tweetner7-continuous`](https://huggingface.co/tner/bertweet-large-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 66.41 | 61.66 | 55.07 | 46.85 | 48.16 | 61.44 | 68.87 | 84.04 | 67.18 | 65.88 | 61.82 | 58.38 | 54.65 | 46.12 | 56.39 | 66.67 | 83.89 | 66.67 | 78.97 | 76.42 |
| [`tner/bertweet-base-tweetner7-continuous`](https://huggingface.co/tner/bertweet-base-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 65.84 | 61.02 | 51.85 | 46.83 | 49.66 | 61.17 | 66.58 | 84.47 | 66.59 | 65.16 | 61.35 | 55.76 | 56.83 | 46.22 | 56.32 | 66.27 | 82.94 | 65.13 | 79.1 | 76.8 |
| [`tner/bert-large-tweetner7-continuous`](https://huggingface.co/tner/bert-large-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 63.2 | 57.67 | 51.4 | 39.74 | 42.55 | 58.6 | 63.36 | 81.27 | 66.78 | 62.48 | 57.87 | 56.56 | 43.65 | 45.51 | 50.38 | 60.26 | 80.62 | 68.12 | 76.04 | 72.46 |
| [`tner/bert-base-tweetner7-continuous`](https://huggingface.co/tner/bert-base-tweetner7-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 61.8 | 56.84 | 47.4 | 38.22 | 44.05 | 57.73 | 64.42 | 80.72 | 65.31 | 61.41 | 57.11 | 54.41 | 42.41 | 41.46 | 51.25 | 63.49 | 79.9 | 66.84 | 76.53 | 72.5 |
| [`tner/roberta-large-tweetner7-2021`](https://huggingface.co/tner/roberta-large-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.05 | 59.11 | 50.58 | 43.91 | 46.6 | 60.68 | 63.99 | 82.68 | 65.3 | 63.36 | 59.15 | 53.22 | 49.41 | 46.61 | 54.65 | 63.12 | 81.33 | 65.67 | 77.71 | 74.36 |
| [`tner/roberta-base-tweetner7-2021`](https://huggingface.co/tner/roberta-base-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 61.76 | 57 | 48.9 | 38 | 45.51 | 57.02 | 65.06 | 81.34 | 63.17 | 60.5 | 56.12 | 49.86 | 45.33 | 39.83 | 52.81 | 60.95 | 79.93 | 64.15 | 76.92 | 73.75 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-2021`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 63.23 | 56.72 | 46.73 | 33.12 | 45.97 | 57.61 | 64.42 | 83.21 | 65.95 | 61.91 | 56.09 | 48.59 | 41.1 | 44.35 | 49.57 | 64.16 | 82.3 | 62.6 | 75.69 | 73.04 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-2021`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 63.98 | 58.91 | 51.04 | 40.86 | 46.2 | 60.22 | 65.55 | 82.64 | 65.88 | 63.07 | 58.51 | 53.26 | 47.09 | 40.92 | 56.46 | 64.86 | 82.1 | 64.89 | 77.87 | 75.35 |
| [`tner/bertweet-large-tweetner7-2021`](https://huggingface.co/tner/bertweet-large-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 62.9 | 58.13 | 48.87 | 42.33 | 44.87 | 56.4 | 66.21 | 81.05 | 67.16 | 61.61 | 56.84 | 54.24 | 40.83 | 43.34 | 50.3 | 64.56 | 81.57 | 63.05 | 76.5 | 74.46 |
| [`tner/bertweet-base-tweetner7-2021`](https://huggingface.co/tner/bertweet-base-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 63.09 | 57.35 | 45.66 | 40.99 | 46.28 | 59.32 | 63.34 | 82.79 | 63.1 | 62.06 | 57.23 | 49.87 | 45.83 | 43.89 | 52.65 | 63.58 | 81.79 | 63.01 | 77.88 | 75.95 |
| [`tner/bert-large-tweetner7-2021`](https://huggingface.co/tner/bert-large-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 59.75 | 53.93 | 44.87 | 34.17 | 40.24 | 55.68 | 63.95 | 79.4 | 59.19 | 56.63 | 50.97 | 49.32 | 31.58 | 30.39 | 50.27 | 59.76 | 76.07 | 59.41 | 74.98 | 70.66 |
| [`tner/bert-base-tweetner7-2021`](https://huggingface.co/tner/bert-base-tweetner7-2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 60.67 | 55.5 | 46.8 | 35.35 | 41.28 | 56.23 | 64.78 | 79.89 | 64.17 | 58.45 | 54.22 | 48.84 | 43.05 | 32.27 | 50.65 | 61.54 | 76.68 | 66.5 | 75.72 | 70.86 |
| [`tner/roberta-large-tweetner7-2020`](https://huggingface.co/tner/roberta-large-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.76 | 60 | 52.23 | 45.89 | 48.51 | 60.88 | 64.43 | 83.32 | 64.75 | 65.67 | 61.88 | 56.82 | 51.85 | 51.06 | 58.65 | 67.06 | 82.59 | 65.15 | 78.36 | 76.11 |
| [`tner/roberta-base-tweetner7-2020`](https://huggingface.co/tner/roberta-base-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 64.21 | 59.11 | 50.75 | 44.44 | 43.9 | 59.15 | 65.84 | 83.92 | 65.73 | 64.25 | 60.23 | 58.59 | 48.94 | 43.84 | 55.31 | 65.63 | 82 | 67.32 | 77.89 | 74.8 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-2020`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 64.28 | 59.31 | 48.54 | 46.89 | 43.69 | 59.09 | 67.01 | 84 | 65.98 | 65.42 | 61.11 | 56.28 | 53.69 | 43.39 | 56.23 | 64.76 | 84.73 | 68.72 | 77.9 | 76.56 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-2020`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 62.87 | 58.26 | 49.9 | 44.9 | 43.68 | 57.62 | 64.38 | 82.29 | 65.07 | 64.39 | 60.31 | 55.19 | 51.72 | 42.91 | 55.95 | 65.47 | 83.98 | 66.98 | 76.49 | 75.65 |
| [`tner/bertweet-large-tweetner7-2020`](https://huggingface.co/tner/bertweet-large-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 64.01 | 59.47 | 52.29 | 46.3 | 45 | 59.27 | 65.53 | 82.73 | 65.19 | 65.93 | 62.61 | 59.67 | 58.92 | 45.01 | 54.55 | 68.09 | 83.59 | 68.47 | 78.26 | 77.38 |
| [`tner/bertweet-base-tweetner7-2020`](https://huggingface.co/tner/bertweet-base-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 64.06 | 59.44 | 51.62 | 45.72 | 45.87 | 59.74 | 64.7 | 82.71 | 65.74 | 66.38 | 62.41 | 58.05 | 54.95 | 49.9 | 56.18 | 67.45 | 84.75 | 65.57 | 77.91 | 77.73 |
| [`tner/bert-large-tweetner7-2020`](https://huggingface.co/tner/bert-large-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 61.43 | 56.14 | 50.11 | 39.03 | 41.8 | 57.31 | 61.13 | 80.6 | 63.02 | 62.19 | 58.15 | 56.68 | 43.75 | 47.24 | 49.72 | 62.62 | 80.03 | 66.97 | 75.86 | 73.79 |
| [`tner/bert-base-tweetner7-2020`](https://huggingface.co/tner/bert-base-tweetner7-2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 60.09 | 54.67 | 44.11 | 37.52 | 40.28 | 55.77 | 61.8 | 80.52 | 62.73 | 60.87 | 56.49 | 50.77 | 44.07 | 38.35 | 53.18 | 63.29 | 81.06 | 64.71 | 75.61 | 72.42 |
Model description follows below.
* Model with suffix `-all`: Model fine-tuned on `train_all` and validated on `validation_2021`.
* Model with suffix `-continuous`: Model fine-tuned on `train_2021` continuously after fine-tuning on `train_2020` and validated on `validation_2021`.
* Model with suffix `-2021`: Model fine-tuned only on `train_2021` and validated on `validation_2021`.
* Model with suffix `-2020`: Model fine-tuned only on `train_2021` and validated on `validation_2020`.
### Sub Models (used in ablation study)
- Model fine-tuned only on `train_random` and validated on `validation_2020`.
| Model (link) | Data | Language Model | Micro F1 (2021) | Macro F1 (2021) | F1 (2021)/corporation | F1 (2021)/creative_work | F1 (2021)/event | F1 (2021)/group | F1 (2021)/location | F1 (2021)/person | F1 (2021)/product | Micro F1 (2020) | Macro F1 (2020) | F1 (2020)/corporation | F1 (2020)/creative_work | F1 (2020)/event | F1 (2020)/group | F1 (2020)/location | F1 (2020)/person | F1 (2020)/product | Entity-Span F1 (2021) | Entity-Span F1 (2020) |
|:------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------------:|------------------------:|
| [`tner/roberta-large-tweetner7-random`](https://huggingface.co/tner/roberta-large-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 66.33 | 60.96 | 52.24 | 45.19 | 48.95 | 63.28 | 66.92 | 83.84 | 66.34 | 64.4 | 60.09 | 53.45 | 50.27 | 46.68 | 57.25 | 65.44 | 81.79 | 65.73 | 79 | 75.52 |
| [`tner/roberta-base-tweetner7-random`](https://huggingface.co/tner/roberta-base-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-base`](https://huggingface.co/roberta-base) | 64.04 | 59.23 | 50.73 | 42.35 | 45.98 | 59.73 | 67.95 | 82.32 | 65.58 | 64.14 | 59.78 | 57.58 | 47.62 | 42.19 | 56.48 | 67.07 | 82.71 | 64.84 | 78.04 | 74.26 |
| [`tner/twitter-roberta-base-2019-90m-tweetner7-random`](https://huggingface.co/tner/twitter-roberta-base-2019-90m-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-2019-90m`](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) | 63.29 | 58.5 | 50.56 | 41.68 | 45.7 | 59.91 | 64.8 | 83.02 | 63.82 | 64.29 | 60.67 | 56.85 | 48.88 | 45.36 | 55.03 | 71.75 | 82.29 | 64.55 | 77.36 | 76.21 |
| [`tner/twitter-roberta-base-dec2020-tweetner7-random`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2020`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020) | 64.72 | 59.97 | 49.08 | 46.42 | 45.65 | 61.68 | 67.5 | 83.31 | 66.15 | 64.69 | 60.53 | 55.56 | 53.85 | 44.27 | 56.57 | 65.05 | 84.03 | 64.41 | 78.29 | 75.94 |
| [`tner/bertweet-large-tweetner7-random`](https://huggingface.co/tner/bertweet-large-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large`](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021vinai/bertweet-large) | 64.86 | 60.49 | 53.59 | 45.47 | 46.19 | 61.64 | 66.16 | 82.79 | 67.58 | 66.02 | 62.72 | 57.81 | 58.19 | 47.64 | 58.78 | 68.25 | 83.36 | 64.97 | 78.43 | 77.2 |
| [`tner/bertweet-base-tweetner7-random`](https://huggingface.co/tner/bertweet-base-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`vinai/bertweet-base`](https://huggingface.co/vinai/bertweet-base) | 65.55 | 59.58 | 49.6 | 40.06 | 47.29 | 62.07 | 67.98 | 83.52 | 66.56 | 63.89 | 58.61 | 54.38 | 45.05 | 41.97 | 55.88 | 66.03 | 83.36 | 63.61 | 77.8 | 74.39 |
| [`tner/bert-large-tweetner7-random`](https://huggingface.co/tner/bert-large-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-large`](https://huggingface.co/bert-large) | 62.39 | 57.54 | 49.15 | 39.72 | 44.79 | 57.67 | 67.22 | 81.17 | 63.07 | 61.54 | 57.09 | 56.34 | 42.81 | 42.69 | 53.36 | 61.98 | 81.04 | 61.43 | 76.49 | 73.29 |
| [`tner/bert-base-tweetner7-random`](https://huggingface.co/tner/bert-base-tweetner7-random) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`bert-base`](https://huggingface.co/bert-base) | 60.91 | 55.92 | 46.51 | 39.05 | 41.83 | 56.14 | 63.9 | 80.45 | 63.54 | 61.04 | 56.75 | 53.94 | 42.77 | 39.15 | 53.07 | 62.67 | 80.59 | 65.08 | 75.72 | 72.73 |
- Model fine-tuned on the self-labeled dataset on `extra_{2020,2021}` and validated on `validation_2020`.
| Model (link) | Data | Language Model | Micro F1 (2021) | Macro F1 (2021) | F1 (2021)/corporation | F1 (2021)/creative_work | F1 (2021)/event | F1 (2021)/group | F1 (2021)/location | F1 (2021)/person | F1 (2021)/product | Micro F1 (2020) | Macro F1 (2020) | F1 (2020)/corporation | F1 (2020)/creative_work | F1 (2020)/event | F1 (2020)/group | F1 (2020)/location | F1 (2020)/person | F1 (2020)/product | Entity-Span F1 (2021) | Entity-Span F1 (2020) |
|:----------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:--------------------------------------------------------|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------------:|------------------------:|
| [`tner/roberta-large-tweetner7-selflabel2020`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.56 | 59.63 | 52.28 | 46.82 | 44.47 | 61.55 | 64.24 | 84.02 | 64.02 | 65.9 | 61.85 | 58.15 | 51.99 | 48.05 | 57.25 | 66.86 | 84.16 | 66.51 | 78.46 | 76.71 |
| [`tner/roberta-large-tweetner7-selflabel2021`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2021) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.6 | 59.45 | 50.21 | 45.89 | 45.18 | 60.3 | 66.71 | 83.46 | 64.38 | 64.75 | 60.65 | 56.19 | 50.41 | 47.31 | 55.21 | 67.46 | 81.9 | 66.06 | 78.57 | 76.63 |
| [`tner/roberta-large-tweetner7-2020-selflabel2020-all`](https://huggingface.co/tner/roberta-large-tweetner7-2020-selflabel2020-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 65.46 | 60.39 | 52.56 | 46.12 | 45.83 | 61.7 | 67.17 | 84.39 | 64.95 | 66.23 | 62.26 | 57.5 | 54.2 | 46.75 | 58.32 | 67.86 | 83.56 | 67.61 | 79.17 | 77.17 |
| [`tner/roberta-large-tweetner7-2020-selflabel2021-all`](https://huggingface.co/tner/roberta-large-tweetner7-2020-selflabel2021-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.52 | 59.45 | 50.67 | 45.38 | 44.53 | 60.63 | 66.19 | 83.59 | 65.17 | 66.05 | 61.83 | 58.23 | 53.44 | 44.39 | 59.79 | 68.09 | 83.43 | 65.43 | 78.5 | 76.94 |
| [`tner/roberta-large-tweetner7-selflabel2020-continuous`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 65.15 | 60.23 | 52.53 | 46.5 | 46.18 | 60.87 | 66.67 | 83.83 | 65.03 | 66.7 | 62.86 | 59.35 | 54.44 | 48.28 | 59.44 | 67.66 | 83.36 | 67.45 | 78.73 | 77.12 |
| [`tner/roberta-large-tweetner7-selflabel2021-continuous`](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2021-continuous) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`roberta-large`](https://huggingface.co/roberta-large) | 64.48 | 59.41 | 50.58 | 45.67 | 44.4 | 61.09 | 66.36 | 83.63 | 64.14 | 65.48 | 61.42 | 56.93 | 51.75 | 48.72 | 57.61 | 67.27 | 83.29 | 64.37 | 78.36 | 76.5 |
Model description follows below.
* Model with suffix `-self2020`: Fine-tuning on the self-annotated data of `extra_2020` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7).
* Model with suffix `-self2021`: Fine-tuning on the self-annotated data of `extra_2021` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7).
* Model with suffix `-2020-self2020-all`: Fine-tuning on the self-annotated data of `extra_2020` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Combined training dataset of `extra_2020` and `train_2020`.
* Model with suffix `-2020-self2021-all`: Fine-tuning on the self-annotated data of `extra_2021` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Combined training dataset of `extra_2021` and `train_2020`.
* Model with suffix `-2020-self2020-continuous`: Fine-tuning on the self-annotated data of `extra_2020` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Fine-tuning on `train_2020` and continuing fine-tuning on `extra_2020`.
* Model with suffix `-2020-self2021-continuous`: Fine-tuning on the self-annotated data of `extra_2021` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7). Fine-tuning on `train_2020` and continuing fine-tuning on `extra_2020`.
### Reproduce Experimental Result
To reproduce the experimental result on our AACL paper, please see the repository
[https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).
## Citation Information
```
@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",
}
```
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