model update
Browse files- README.md +88 -0
- best_run_hyperparameters.json +1 -0
- metric.json +1 -0
README.md
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---
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datasets:
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- cardiffnlp/tweet_sentiment_multilingual
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metrics:
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- f1
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- accuracy
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model-index:
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- name: cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: cardiffnlp/tweet_sentiment_multilingual
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type: all
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split: test
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metrics:
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- name: Micro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
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type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
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value: 0.6931034482758621
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- name: Macro F1 (cardiffnlp/tweet_sentiment_multilingual/all)
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type: micro_f1_cardiffnlp/tweet_sentiment_multilingual/all
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value: 0.692628774202147
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- name: Accuracy (cardiffnlp/tweet_sentiment_multilingual/all)
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type: accuracy_cardiffnlp/tweet_sentiment_multilingual/all
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value: 0.6931034482758621
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pipeline_tag: text-classification
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widget:
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- text: Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}
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example_title: "topic_classification 1"
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- text: Yes, including Medicare and social security saving👍
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example_title: "sentiment 1"
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- text: All two of them taste like ass.
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example_title: "offensive 1"
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- text: If you wanna look like a badass, have drama on social media
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example_title: "irony 1"
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- text: Whoever just unfollowed me you a bitch
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example_title: "hate 1"
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- text: I love swimming for the same reason I love meditating...the feeling of weightlessness.
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example_title: "emotion 1"
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- text: Beautiful sunset last night from the pontoon @TupperLakeNY
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example_title: "emoji 1"
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---
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# cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual
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This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) on the
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[`cardiffnlp/tweet_sentiment_multilingual (all)`](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)
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via [`tweetnlp`](https://github.com/cardiffnlp/tweetnlp).
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Training split is `train` and parameters have been tuned on the validation split `validation`.
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Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual/raw/main/metric.json)).
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- F1 (micro): 0.6931034482758621
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- F1 (macro): 0.692628774202147
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- Accuracy: 0.6931034482758621
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### Usage
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Install tweetnlp via pip.
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```shell
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pip install tweetnlp
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```
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Load the model in python.
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```python
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import tweetnlp
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model = tweetnlp.Classifier("cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual", max_length=128)
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model.predict('Get the all-analog Classic Vinyl Edition of "Takin Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below {{URL}}')
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```
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### Reference
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```
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@inproceedings{dimosthenis-etal-2022-twitter,
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title = "{T}witter {T}opic {C}lassification",
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author = "Antypas, Dimosthenis and
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Ushio, Asahi and
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Camacho-Collados, Jose and
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Neves, Leonardo and
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Silva, Vitor and
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Barbieri, Francesco",
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
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month = oct,
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year = "2022",
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address = "Gyeongju, Republic of Korea",
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publisher = "International Committee on Computational Linguistics"
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}
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```
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best_run_hyperparameters.json
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{"learning_rate": 5.61151641533451e-06, "num_train_epochs": 5, "per_device_train_batch_size": 32}
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metric.json
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{"eval_loss": 0.8723608255386353, "eval_f1": 0.6931034482758621, "eval_f1_macro": 0.692628774202147, "eval_accuracy": 0.6931034482758621, "eval_runtime": 18.2192, "eval_samples_per_second": 382.016, "eval_steps_per_second": 47.752}
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