Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
Tags:
License:
readme
Browse files- .gitignore +1 -0
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For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
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In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
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### Models
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Following models are fine-tuned on `train_all` and validated on `validation_2021` of `tner/tweetner7`. See full model list [here](https://github.com/asahi417/tner/blob/master/MODEL_CARD.md#models-for-tweetner7).
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| Model (link) | Data | Language Model |
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| [`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) |
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| [`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) |
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| [`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) | [`twitter-roberta-base-2019-90m`](https://huggingface.co/twitter-roberta-base-2019-90m) |
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| [`tner/twitter-roberta-base-dec2020-tweetner7-all`](https://huggingface.co/tner/twitter-roberta-base-dec2020-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`twitter-roberta-base-dec2020`](https://huggingface.co/twitter-roberta-base-dec2020) |
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| [`tner/twitter-roberta-base-dec2021-tweetner7-all`](https://huggingface.co/tner/twitter-roberta-base-dec2021-tweetner7-all) | [`tweetner7`](https://huggingface.co/datasets/tner/tweetner7) | [`twitter-roberta-base-dec2021`](https://huggingface.co/twitter-roberta-base-dec2021) |
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### Reproduce Experimental Result
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To reproduce the experimental result on our AACL paper, please see the repository
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[https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).
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## Dataset Structure
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### Data Instances
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```
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@inproceedings{ushio-etal-2022-tweet,
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For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
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In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
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## Dataset Structure
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### Data Instances
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```
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## Models
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| 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) |
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|:--------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------------:|------------------------:|
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| 149 |
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| [`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 |
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| 150 |
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| [`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 |
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| 151 |
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| [`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 |
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| [`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 |
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| 153 |
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| [`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 |
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| [`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 |
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| [`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 |
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| 156 |
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| [`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 |
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| [`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 |
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| 158 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| [`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 |
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| 165 |
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| [`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 |
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| 166 |
+
| [`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 |
|
| 167 |
+
| [`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 |
|
| 168 |
+
| [`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 |
|
| 169 |
+
| [`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 |
|
| 170 |
+
| [`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 |
|
| 171 |
+
| [`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 |
|
| 172 |
+
|
| 173 |
+
Model description follows below.
|
| 174 |
+
* Model with suffix `-all`: Model fine-tuned on `train_all` and validated on `validation_2021`.
|
| 175 |
+
* Model with suffix `-continuous`: Model fine-tuned on `train_2021` continuously after fine-tuning on `train_2020` and validated on `validation_2021`.
|
| 176 |
+
* Model with suffix `-2021`: Model fine-tuned only on `train_2021` and validated on `validation_2021`.
|
| 177 |
+
* Model with suffix `-2020`: Model fine-tuned only on `train_2021` and validated on `validation_2020`.
|
| 178 |
+
|
| 179 |
+
|
| 180 |
+
### Sub Models (used in ablation study)
|
| 181 |
+
|
| 182 |
+
- Model fine-tuned only on `train_random` and validated on `validation_2020`.
|
| 183 |
+
|
| 184 |
+
| 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) |
|
| 185 |
+
|:------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------------:|------------------------:|
|
| 186 |
+
| [`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 |
|
| 187 |
+
| [`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 |
|
| 188 |
+
| [`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 |
|
| 189 |
+
| [`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 |
|
| 190 |
+
| [`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 |
|
| 191 |
+
| [`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 |
|
| 192 |
+
| [`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 |
|
| 193 |
+
| [`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 |
|
| 194 |
+
|
| 195 |
+
- Model fine-tuned on the self-labeled dataset on `extra_{2020,2021}` and validated on `validation_2020`.
|
| 196 |
+
|
| 197 |
+
| 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) |
|
| 198 |
+
|:----------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------|:--------------------------------------------------------|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------:|------------------:|------------------------:|--------------------------:|------------------:|------------------:|---------------------:|-------------------:|--------------------:|------------------------:|------------------------:|
|
| 199 |
+
| [`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 |
|
| 200 |
+
| [`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 |
|
| 201 |
+
| [`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 |
|
| 202 |
+
| [`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 |
|
| 203 |
+
| [`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 |
|
| 204 |
+
| [`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 |
|
| 205 |
+
|
| 206 |
+
|
| 207 |
+
Model description follows below.
|
| 208 |
+
* Model with suffix `-self2020`: Fine-tuning on the self-annotated data of `extra_2020` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7).
|
| 209 |
+
* Model with suffix `-self2021`: Fine-tuning on the self-annotated data of `extra_2021` split of [tweetner7](https://huggingface.co/datasets/tner/tweetner7).
|
| 210 |
+
* 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`.
|
| 211 |
+
* 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`.
|
| 212 |
+
* 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`.
|
| 213 |
+
* 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`.
|
| 214 |
+
|
| 215 |
+
### Reproduce Experimental Result
|
| 216 |
+
|
| 217 |
+
To reproduce the experimental result on our AACL paper, please see the repository
|
| 218 |
+
[https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).
|
| 219 |
+
|
| 220 |
+
|
| 221 |
+
## Citation Information
|
| 222 |
|
| 223 |
```
|
| 224 |
@inproceedings{ushio-etal-2022-tweet,
|
get_model_list.py
CHANGED
|
@@ -4,45 +4,86 @@ import requests
|
|
| 4 |
|
| 5 |
import pandas as pd
|
| 6 |
|
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|
| 7 |
|
| 8 |
-
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|
|
|
| 9 |
try:
|
| 10 |
with open(filename) as f:
|
| 11 |
-
json.load(f)
|
| 12 |
except Exception:
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
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|
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|
|
|
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|
|
|
|
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|
|
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|
|
|
|
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|
| 19 |
return tmp
|
| 20 |
|
| 21 |
|
| 22 |
-
|
|
|
|
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|
|
|
|
|
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| 23 |
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
|
| 35 |
]
|
| 36 |
|
| 37 |
-
os.makedirs("metric_files", exist_ok=True)
|
| 38 |
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
|
|
|
|
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|
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|
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|
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|
|
|
|
| 46 |
|
| 47 |
-
df = pd.DataFrame(metrics)
|
| 48 |
-
print(
|
|
|
|
|
|
| 4 |
|
| 5 |
import pandas as pd
|
| 6 |
|
| 7 |
+
dataset_link = "[`tweetner7`](https://huggingface.co/datasets/tner/tweetner7)"
|
| 8 |
+
metric_dir = 'metric_files'
|
| 9 |
+
os.makedirs(metric_dir, exist_ok=True)
|
| 10 |
|
| 11 |
+
|
| 12 |
+
def lm_link(_model): return f"[`{_model}`](https://huggingface.co/{_model})"
|
| 13 |
+
|
| 14 |
+
|
| 15 |
+
def model_link(_model, _type): return f"[`tner/{_model}-tweetner7-{_type}`](https://huggingface.co/tner/{_model}-tweetner7-{_type})"
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
def download(_model, _type):
|
| 19 |
+
url = f"https://huggingface.co/tner/{_model}-tweetner7-{_type}/raw/main/eval"
|
| 20 |
+
filename = f"{metric_dir}/{_model}-{_type}.json"
|
| 21 |
+
print(url, filename)
|
| 22 |
try:
|
| 23 |
with open(filename) as f:
|
| 24 |
+
return json.load(f)
|
| 25 |
except Exception:
|
| 26 |
+
tmp = {}
|
| 27 |
+
for metric in ["metric.test_2021", "metric.test_2020", "metric_span.test_2021", "metric_span.test_2020"]:
|
| 28 |
+
year = metric[-4:]
|
| 29 |
+
if metric not in tmp:
|
| 30 |
+
_metric = json.loads(requests.get(f"{url}/{metric}.json").content)
|
| 31 |
+
if '_span' in metric:
|
| 32 |
+
tmp[f"Entity-Span F1 ({year})"] = round(100 * _metric["micro/f1"], 2)
|
| 33 |
+
else:
|
| 34 |
+
tmp[f"Micro F1 ({year})"] = round(100 * _metric["micro/f1"], 2)
|
| 35 |
+
tmp[f"Macro F1 ({year})"] = round(100 * _metric["macro/f1"], 2)
|
| 36 |
+
tmp.update({f"F1 ({year})/{k}": round(100 * v['f1'], 2) for k, v in _metric["per_entity_metric"].items()})
|
| 37 |
+
with open(filename, "w") as f:
|
| 38 |
+
json.dump(tmp, f)
|
| 39 |
return tmp
|
| 40 |
|
| 41 |
|
| 42 |
+
lms = [
|
| 43 |
+
"roberta-large",
|
| 44 |
+
"roberta-base",
|
| 45 |
+
"cardiffnlp/twitter-roberta-base-2019-90m",
|
| 46 |
+
"cardiffnlp/twitter-roberta-base-dec2020",
|
| 47 |
+
"cardiffnlp/twitter-roberta-base-dec2021"
|
| 48 |
+
"vinai/bertweet-large",
|
| 49 |
+
"vinai/bertweet-base",
|
| 50 |
+
"bert-large",
|
| 51 |
+
"bert-base"
|
| 52 |
+
]
|
| 53 |
|
| 54 |
+
types = [
|
| 55 |
+
["all", "continuous", "2021", "2020"],
|
| 56 |
+
["random"],
|
| 57 |
+
[
|
| 58 |
+
"selflabel2020",
|
| 59 |
+
"selflabel2021",
|
| 60 |
+
"2020-selflabel2020-all",
|
| 61 |
+
"2020-selflabel2021-all",
|
| 62 |
+
"selflabel2020-continuous",
|
| 63 |
+
"selflabel2021-continuous"
|
| 64 |
+
]
|
| 65 |
]
|
| 66 |
|
|
|
|
| 67 |
|
| 68 |
+
for tt in types:
|
| 69 |
+
metrics = []
|
| 70 |
+
for t in tt:
|
| 71 |
+
for lm in lms:
|
| 72 |
+
|
| 73 |
+
if 'selflabel' in t and lm != "roberta-large":
|
| 74 |
+
continue
|
| 75 |
+
_lm_link = lm_link(lm)
|
| 76 |
+
lm = os.path.basename(lm)
|
| 77 |
+
_model_link = model_link(lm, t)
|
| 78 |
+
__metric = {
|
| 79 |
+
"Model (link)": model_link(lm, t),
|
| 80 |
+
"Data": dataset_link,
|
| 81 |
+
"Language Model": _lm_link
|
| 82 |
+
}
|
| 83 |
+
__metric.update(download(lm, t))
|
| 84 |
+
metrics.append(__metric)
|
| 85 |
|
| 86 |
+
df = pd.DataFrame(metrics)
|
| 87 |
+
print(tt)
|
| 88 |
+
print(df.to_markdown(index=False))
|
| 89 |
+
print()
|