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model update

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+ ---
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+ datasets:
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+ - cardiffnlp/tweet_topic_multi
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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-multi-2020
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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_multi
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+ type: cardiffnlp/tweet_topic_multi
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+ args: cardiffnlp/tweet_topic_multi
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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: 0.731106471816284
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+ - name: F1 (macro)
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+ type: f1_macro
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+ value: 0.532141354814677
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.509827278141751
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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-multi-2020
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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_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). This model is fine-tuned on `train_2020` 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_multi/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): 0.731106471816284
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+ - F1 (macro): 0.532141354814677
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+ - Accuracy: 0.509827278141751
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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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+ import math
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+ import torch
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+ from transformers import AutoModelForSequenceClassification, AutoTokenizer
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+
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+ def sigmoid(x):
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+ return 1 / (1 + math.exp(-x))
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+
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+ tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020")
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+ model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020", problem_type="multi_label_classification")
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+ model.eval()
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+ class_mapping = model.config.id2label
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+
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+ with torch.no_grad():
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+ text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
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+ tokens = tokenizer(text, return_tensors='pt')
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+ output = model(**tokens)
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+ flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
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+ topic = [class_mapping[n] for n, i in enumerate(flags) if i]
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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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+
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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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+ ```