Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
ArXiv:
License:
Delete loading script auxiliary file
Browse files
readme.py
DELETED
@@ -1,104 +0,0 @@
|
|
1 |
-
import os
|
2 |
-
import json
|
3 |
-
from typing import Dict
|
4 |
-
|
5 |
-
|
6 |
-
sample = "#NewVideo Cray Dollas- Water- Ft. Charlie Rose- (Official Music Video)- {{URL}} via {@YouTube@} #watchandlearn {{USERNAME}}"
|
7 |
-
bib = """
|
8 |
-
@inproceedings{dimosthenis-etal-2022-twitter,
|
9 |
-
title = "{T}witter {T}opic {C}lassification",
|
10 |
-
author = "Antypas, Dimosthenis and
|
11 |
-
Ushio, Asahi and
|
12 |
-
Camacho-Collados, Jose and
|
13 |
-
Neves, Leonardo and
|
14 |
-
Silva, Vitor and
|
15 |
-
Barbieri, Francesco",
|
16 |
-
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
|
17 |
-
month = oct,
|
18 |
-
year = "2022",
|
19 |
-
address = "Gyeongju, Republic of Korea",
|
20 |
-
publisher = "International Committee on Computational Linguistics"
|
21 |
-
}
|
22 |
-
"""
|
23 |
-
|
24 |
-
|
25 |
-
def get_readme(model_name: str,
|
26 |
-
metric: str,
|
27 |
-
language_model,
|
28 |
-
extra_desc: str = ''):
|
29 |
-
with open(metric) as f:
|
30 |
-
metric = json.load(f)
|
31 |
-
return f"""---
|
32 |
-
datasets:
|
33 |
-
- cardiffnlp/tweet_topic_multi
|
34 |
-
metrics:
|
35 |
-
- f1
|
36 |
-
- accuracy
|
37 |
-
model-index:
|
38 |
-
- name: {model_name}
|
39 |
-
results:
|
40 |
-
- task:
|
41 |
-
type: text-classification
|
42 |
-
name: Text Classification
|
43 |
-
dataset:
|
44 |
-
name: cardiffnlp/tweet_topic_multi
|
45 |
-
type: cardiffnlp/tweet_topic_multi
|
46 |
-
args: cardiffnlp/tweet_topic_multi
|
47 |
-
split: test_2021
|
48 |
-
metrics:
|
49 |
-
- name: F1
|
50 |
-
type: f1
|
51 |
-
value: {metric['test/eval_f1']}
|
52 |
-
- name: F1 (macro)
|
53 |
-
type: f1_macro
|
54 |
-
value: {metric['test/eval_f1_macro']}
|
55 |
-
- name: Accuracy
|
56 |
-
type: accuracy
|
57 |
-
value: {metric['test/eval_accuracy']}
|
58 |
-
pipeline_tag: text-classification
|
59 |
-
widget:
|
60 |
-
- 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"
|
61 |
-
example_title: "Example 1"
|
62 |
-
- 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."
|
63 |
-
example_title: "Example 2"
|
64 |
-
---
|
65 |
-
# {model_name}
|
66 |
-
|
67 |
-
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}
|
68 |
-
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:
|
69 |
-
|
70 |
-
- F1 (micro): {metric['test/eval_f1']}
|
71 |
-
- F1 (macro): {metric['test/eval_f1_macro']}
|
72 |
-
- Accuracy: {metric['test/eval_accuracy']}
|
73 |
-
|
74 |
-
|
75 |
-
### Usage
|
76 |
-
|
77 |
-
```python
|
78 |
-
import math
|
79 |
-
import torch
|
80 |
-
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
81 |
-
|
82 |
-
def sigmoid(x):
|
83 |
-
return 1 / (1 + math.exp(-x))
|
84 |
-
|
85 |
-
tokenizer = AutoTokenizer.from_pretrained("{model_name}")
|
86 |
-
model = AutoModelForSequenceClassification.from_pretrained("{model_name}", problem_type="multi_label_classification")
|
87 |
-
model.eval()
|
88 |
-
class_mapping = model.config.id2label
|
89 |
-
|
90 |
-
with torch.no_grad():
|
91 |
-
text = {sample}
|
92 |
-
tokens = tokenizer(text, return_tensors='pt')
|
93 |
-
output = model(**tokens)
|
94 |
-
flags = [sigmoid(s) > 0.5 for s in output[0][0].detach().tolist()]
|
95 |
-
topic = [class_mapping[n] for n, i in enumerate(flags) if i]
|
96 |
-
print(topic)
|
97 |
-
```
|
98 |
-
|
99 |
-
### Reference
|
100 |
-
|
101 |
-
```
|
102 |
-
{bib}
|
103 |
-
```
|
104 |
-
"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|