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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}
```
"""
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