Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
License:
| import json | |
| import os | |
| import requests | |
| import pandas as pd | |
| def download(filename, url): | |
| try: | |
| with open(filename) as f: | |
| json.load(f) | |
| except Exception: | |
| os.makedirs(os.path.dirname(filename), exist_ok=True) | |
| with open(filename, "wb") as f: | |
| r = requests.get(url) | |
| f.write(r.content) | |
| with open(filename) as f: | |
| tmp = json.load(f) | |
| return tmp | |
| models = [ | |
| "cardiffnlp/roberta-large-tweet-topic-multi-all", | |
| "cardiffnlp/roberta-base-tweet-topic-multi-all", | |
| "cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-all", | |
| "cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-all", | |
| "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-all", | |
| "cardiffnlp/roberta-large-tweet-topic-multi-2020", | |
| "cardiffnlp/roberta-base-tweet-topic-multi-2020", | |
| "cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-multi-2020", | |
| "cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-multi-2020", | |
| "cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-multi-2020" | |
| ] | |
| os.makedirs("metric_files", exist_ok=True) | |
| metrics = [] | |
| for i in models: | |
| model_type = "all (2020 + 2021)" if i.endswith("all") else "2020 only" | |
| url = f"https://huggingface.co/{i}/raw/main/metric_summary.json" | |
| model_url = f"https://huggingface.co/{i}" | |
| metric = download(f"metric_files/{os.path.basename(i)}.json", url) | |
| metrics.append({"model": f"[{i}]({model_url})", "training data": model_type, "F1": metric["test/eval_f1"], "F1 (macro)": metric["test/eval_f1_macro"], "Accuracy": metric["test/eval_accuracy"]}) | |
| df = pd.DataFrame(metrics) | |
| print(df.to_markdown(index=False)) | |