model update
Browse files- README.md +73 -0
- metric_summary.json +1 -0
README.md
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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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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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- F1 (micro): 0.1494388659184879
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- F1 (macro): 0.07391536194788544
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- Accuracy: 0.1494388659184879
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### Usage
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```python
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from transformers import pipeline
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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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### 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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metric_summary.json
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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}
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