Delete loading script auxiliary file

readme.py

@@ -1,104 +0,0 @@
-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}
-```
-"""