Update readme.py

readme.py

@@ -30,7 +30,7 @@ def get_readme(model_name: str,
       metric = json.load(f)
     return f"""---
 datasets:
-- cardiffnlp/tweet_topic_multi
+- cardiffnlp/tweet_topic_single
 metrics:
 - f1
 - accuracy
@@ -41,9 +41,9 @@ model-index:
       type: text-classification
       name: Text Classification
     dataset:
-      name: cardiffnlp/tweet_topic_multi
-      type: cardiffnlp/tweet_topic_multi
-      args: cardiffnlp/tweet_topic_multi
+      name: cardiffnlp/tweet_topic_single
+      type: cardiffnlp/tweet_topic_single
+      args: cardiffnlp/tweet_topic_single
       split: test_2021 
     metrics:
     - name: F1
@@ -64,8 +64,8 @@ widget:
 ---
 # {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:
+This model is a fine-tuned version of [{language_model}](https://huggingface.co/{language_model}) on the [tweet_topic_single](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single). {extra_desc}
+Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/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']}
@@ -75,30 +75,14 @@ Fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnl
 ### Usage
 
 ```python
-import math
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers import pipeline
 
-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]
+pipe = pipeline("text-classification", "cardiffnlp/tweet-topic-19-single")  
+topic = pipe("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.")
 print(topic)
 ```
 
 ### Reference
-If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
-
 ```
 {bib}
 ```