Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
Tags:
License:
File size: 2,910 Bytes
f6697c6 74c9f62 f6697c6 74c9f62 f6697c6 74c9f62 f6697c6 74c9f62 f6697c6 3131349 74c9f62 f6697c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
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_single
metrics:
- f1
- accuracy
model-index:
- name: {model_name}
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: cardiffnlp/tweet_topic_single
type: cardiffnlp/tweet_topic_single
args: cardiffnlp/tweet_topic_single
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_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). {extra_desc}
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:
- F1 (micro): {metric['test/eval_f1']}
- F1 (macro): {metric['test/eval_f1_macro']}
- Accuracy: {metric['test/eval_accuracy']}
### Usage
```python
from transformers import pipeline
pipe = pipeline("text-classification", "{model_name}")
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.")
print(topic)
```
### Reference
```
{bib}
```
""" |