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model update

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  1. README.md +88 -0
  2. best_run_hyperparameters.json +1 -0
  3. metric.json +1 -0
README.md ADDED
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+ ---
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+ datasets:
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+ - tweet_eval
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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/roberta-base-sentiment
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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: tweet_eval
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+ type: sentiment
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+ split: test
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+ metrics:
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+ - name: Micro F1 (tweet_eval/sentiment)
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+ type: micro_f1_tweet_eval/sentiment
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+ value: 0.7086453923803321
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+ - name: Macro F1 (tweet_eval/sentiment)
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+ type: micro_f1_tweet_eval/sentiment
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+ value: 0.7097736527692039
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+ - name: Accuracy (tweet_eval/sentiment)
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+ type: accuracy_tweet_eval/sentiment
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+ value: 0.7086453923803321
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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/roberta-base-sentiment
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+
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+ This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the
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+ [`tweet_eval (sentiment)`](https://huggingface.co/datasets/tweet_eval)
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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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+
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+ Following metrics are achieved on the test split `test` ([link](https://huggingface.co/cardiffnlp/roberta-base-sentiment/raw/main/metric.json)).
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+
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+ - F1 (micro): 0.7086453923803321
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+ - F1 (macro): 0.7097736527692039
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+ - Accuracy: 0.7086453923803321
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+
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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/roberta-base-sentiment", 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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+
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+ ### Reference
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+
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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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+
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+
best_run_hyperparameters.json ADDED
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+ {"learning_rate": 1.717762111233842e-05, "num_train_epochs": 2, "per_device_train_batch_size": 16}
metric.json ADDED
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+ {"eval_loss": 0.7201297879219055, "eval_f1": 0.7086453923803321, "eval_f1_macro": 0.7097736527692039, "eval_accuracy": 0.7086453923803321, "eval_runtime": 68.9465, "eval_samples_per_second": 178.167, "eval_steps_per_second": 22.278}