Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
Tags:
License:
| import json | |
| import os | |
| import requests | |
| import pandas as pd | |
| dataset_link = "[`tweetner7`](https://huggingface.co/datasets/tner/tweetner7)" | |
| metric_dir = 'metric_files' | |
| os.makedirs(metric_dir, exist_ok=True) | |
| def lm_link(_model): return f"[`{_model}`](https://huggingface.co/{_model})" | |
| def model_link(_model, _type): return f"[`tner/{_model}-tweetner7-{_type}`](https://huggingface.co/tner/{_model}-tweetner7-{_type})" | |
| def download(_model, _type): | |
| url = f"https://huggingface.co/tner/{_model}-tweetner7-{_type}/raw/main/eval" | |
| filename = f"{metric_dir}/{_model}-{_type}.json" | |
| print(url, filename) | |
| try: | |
| with open(filename) as f: | |
| return json.load(f) | |
| except Exception: | |
| tmp = {} | |
| for metric in ["metric.test_2021", "metric.test_2020", "metric_span.test_2021", "metric_span.test_2020"]: | |
| year = metric[-4:] | |
| if metric not in tmp: | |
| _metric = json.loads(requests.get(f"{url}/{metric}.json").content) | |
| if '_span' in metric: | |
| tmp[f"Entity-Span F1 ({year})"] = round(100 * _metric["micro/f1"], 2) | |
| else: | |
| tmp[f"Micro F1 ({year})"] = round(100 * _metric["micro/f1"], 2) | |
| tmp[f"Macro F1 ({year})"] = round(100 * _metric["macro/f1"], 2) | |
| tmp.update({f"F1 ({year})/{k}": round(100 * v['f1'], 2) for k, v in _metric["per_entity_metric"].items()}) | |
| with open(filename, "w") as f: | |
| json.dump(tmp, f) | |
| return tmp | |
| lms = [ | |
| "roberta-large", | |
| "roberta-base", | |
| "cardiffnlp/twitter-roberta-base-2019-90m", | |
| "cardiffnlp/twitter-roberta-base-dec2020", | |
| "cardiffnlp/twitter-roberta-base-dec2021" | |
| "vinai/bertweet-large", | |
| "vinai/bertweet-base", | |
| "bert-large", | |
| "bert-base" | |
| ] | |
| types = [ | |
| ["all", "continuous", "2021", "2020"], | |
| ["random"], | |
| [ | |
| "selflabel2020", | |
| "selflabel2021", | |
| "2020-selflabel2020-all", | |
| "2020-selflabel2021-all", | |
| "selflabel2020-continuous", | |
| "selflabel2021-continuous" | |
| ] | |
| ] | |
| for tt in types: | |
| metrics = [] | |
| for t in tt: | |
| for lm in lms: | |
| if 'selflabel' in t and lm != "roberta-large": | |
| continue | |
| _lm_link = lm_link(lm) | |
| lm = os.path.basename(lm) | |
| _model_link = model_link(lm, t) | |
| __metric = { | |
| "Model (link)": model_link(lm, t), | |
| "Data": dataset_link, | |
| "Language Model": _lm_link | |
| } | |
| __metric.update(download(lm, t)) | |
| metrics.append(__metric) | |
| df = pd.DataFrame(metrics) | |
| print(tt) | |
| print(df.to_markdown(index=False)) | |
| print() | |