Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
License:
| import os | |
| import json | |
| from typing import Dict | |
| sample = "#NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}" | |
| bib = """ | |
| @inproceedings{dimosthenis-etal-2022-twitter, | |
| title = "{T}witter {T}opic {C}lassification", | |
| author = "Antypas, Dimosthenis and | |
| Ushio, Asahi and | |
| Camacho-Collados, Jose and | |
| Neves, Leonardo and | |
| Silva, Vitor and | |
| Barbieri, Francesco", | |
| booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", | |
| month = oct, | |
| year = "2022", | |
| address = "Gyeongju, Republic of Korea", | |
| publisher = "International Committee on Computational Linguistics" | |
| } | |
| """ | |
| def get_readme(model_name: str, | |
| metric: str, | |
| language_model, | |
| extra_desc: str = ''): | |
| with open(metric) as f: | |
| metric = json.load(f) | |
| return f"""--- | |
| datasets: | |
| - cardiffnlp/tweet_topic_multi | |
| metrics: | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: {model_name} | |
| results: | |
| - task: | |
| type: text-classification | |
| name: Text Classification | |
| dataset: | |
| name: cardiffnlp/tweet_topic_multi | |
| type: cardiffnlp/tweet_topic_multi | |
| args: cardiffnlp/tweet_topic_multi | |
| split: test_2021 | |
| metrics: | |
| - name: F1 | |
| type: f1 | |
| value: {metric['test/eval_f1']} | |
| - name: F1 (macro) | |
| type: f1_macro | |
| value: {metric['test/eval_f1_macro']} | |
| - name: Accuracy | |
| type: accuracy | |
| value: {metric['test/eval_accuracy']} | |
| pipeline_tag: text-classification | |
| widget: | |
| - 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" | |
| example_title: "Example 1" | |
| - 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." | |
| example_title: "Example 2" | |
| --- | |
| # {model_name} | |
| This model is a fine-tuned version of [{language_model}](https://huggingface.co/{language_model}) on the [tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). {extra_desc} | |
| 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: | |
| - F1 (micro): {metric['test/eval_f1']} | |
| - F1 (macro): {metric['test/eval_f1_macro']} | |
| - Accuracy: {metric['test/eval_accuracy']} | |
| ### Usage | |
| ```python | |
| import math | |
| import torch | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| def sigmoid(x): | |
| return 1 / (1 + math.exp(-x)) | |
| tokenizer = AutoTokenizer.from_pretrained("{model_name}") | |
| model = AutoModelForSequenceClassification.from_pretrained("{model_name}", problem_type="multi_label_classification") | |
| model.eval() | |
| class_mapping = model.config.id2label | |
| with torch.no_grad(): | |
| text = {sample} | |
| tokens = tokenizer(text, return_tensors='pt') | |
| output = model(**tokens) | |
| flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()] | |
| topic = [class_mapping[n] for n, i in enumerate(flags) if i] | |
| print(topic) | |
| ``` | |
| ### Reference | |
| ``` | |
| {bib} | |
| ``` | |
| """ | |