Update README.md

README.md

@@ -1,27 +1,36 @@
 ---
-language:
-- en
 license: mit
 datasets:
 - cardiffnlp/super_tweeteval
+language:
+- en
+pipeline_tag: text-classification
 ---
 # cardiffnlp/twitter-roberta-large-intimacy-latest
 
 This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 and finetuned for intimacy analysis (regression on a single text) on the _TweetIntimacy_ dataset of [SuperTweetEval](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
-The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m).
+The original Twitter-large RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-large-2022-154m).
 
 ## Example
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch.nn.functional as F
 
 model_name = "cardiffnlp/twitter-roberta-large-intimacy-latest"
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-
 text= '@user Furthermore, harassment is ILLEGAL in any form!'
-model(**tokenizer(text, return_tensors="pt")).logits
->> tensor([1.6744])
+
+# with pipeline
+pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
+pipe(text)
+>> [{'label': 'LABEL_0', 'score': 0.5246055722236633}]
+
+# alternatively
+logits = model(**tokenizer(text, return_tensors="pt"))
+prob = F.sigmoid(logits.logits).item()
+>> 0.5246055722236633
 ```
 
 ## Citation Information
@@ -34,5 +43,4 @@ Please cite the [reference paper](https://arxiv.org/abs/2310.14757) if you use t
   booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
   year={2023}
 }
-```
-
+```