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model update

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  1. README.md +73 -0
  2. metric_summary.json +1 -0
README.md ADDED
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+ ---
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+ datasets:
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+ - cardiffnlp/tweet_topic_single
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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-roberta-base-dec2021-tweet-topic-single-all
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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_topic_single
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+ type: cardiffnlp/tweet_topic_single
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+ args: cardiffnlp/tweet_topic_single
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+ split: test_2021
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+ metrics:
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+ - name: F1
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+ type: f1
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+ value: 0.1494388659184879
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.07391536194788544
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.1494388659184879
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+ pipeline_tag: text-classification
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+ widget:
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+ - text: "I'm sure the {@Tampa Bay Lightning@} would’ve rather faced the Flyers but man does their experience versus the Blue Jackets this year and last help them a lot versus this Islanders team. Another meat grinder upcoming for the good guys"
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+ example_title: "Example 1"
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+ - text: "Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US."
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+ example_title: "Example 2"
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+ ---
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+ # cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all
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+
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+ This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
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+ Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py). It achieves the following results on the test_2021 set:
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+
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+ - F1 (micro): 0.1494388659184879
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+ - F1 (macro): 0.07391536194788544
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+ - Accuracy: 0.1494388659184879
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+
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+
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+ ### Usage
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ pipe = pipeline("text-classification", "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all")
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+ topic = pipe("Love to take night time bike rides at the jersey shore. Seaside Heights boardwalk. Beautiful weather. Wishing everyone a safe Labor Day weekend in the US.")
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+ print(topic)
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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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+ ```
metric_summary.json ADDED
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+ {"test/eval_loss": 1.7828656435012817, "test/eval_f1": 0.1494388659184879, "test/eval_f1_macro": 0.07391536194788544, "test/eval_accuracy": 0.1494388659184879, "test/eval_runtime": 18.3207, "test/eval_samples_per_second": 92.409, "test/eval_steps_per_second": 11.572}