asahi417's picture
model update
- cardiffnlp/tweet_topic_multi
- f1
- accuracy
- name: cardiffnlp/roberta-large-tweet-topic-multi-all
- task:
type: text-classification
name: Text Classification
name: cardiffnlp/tweet_topic_multi
type: cardiffnlp/tweet_topic_multi
args: cardiffnlp/tweet_topic_multi
split: test_2021
- name: F1
type: f1
value: 0.7631035905901775
- name: F1 (macro)
type: f1_macro
value: 0.6202570779365779
- name: Accuracy
type: accuracy
value: 0.5366289458010721
pipeline_tag: text-classification
- 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"
# cardiffnlp/roberta-large-tweet-topic-multi-all
This model is a fine-tuned version of [roberta-large]( on the [tweet_topic_multi]( This model is fine-tuned on `train_all` split and validated on `test_2021` split of tweet_topic.
Fine-tuning script can be found [here]( It achieves the following results on the test_2021 set:
- F1 (micro): 0.7631035905901775
- F1 (macro): 0.6202570779365779
- Accuracy: 0.5366289458010721
### Usage
import math
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
def sigmoid(x):
return 1 / (1 + math.exp(-x))
tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/roberta-large-tweet-topic-multi-all")
model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/roberta-large-tweet-topic-multi-all", problem_type="multi_label_classification")
class_mapping = model.config.id2label
with torch.no_grad():
text = #NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}
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]
### Reference
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"