Update README.md

README.md

@@ -8,13 +8,13 @@ tags:
 - social
 ---
 
-# Model Card for SocDistilRoBERTa-social
+# Model Card for SocRoBERTa-social
 
 ## Model Description
 
-Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the SocDistilRoBERTa-social language model. A language model that is trained to better classify social texts in the ESG domain.
+Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the SocRoBERTa-social language model. A language model that is trained to better classify social texts in the ESG domain.
 
-Using the [SocDistilRoBERTa-base](https://huggingface.co/ESGBERT/SocDistilRoBERTa-base) model as a starting point, the SocDistilRoBERTa-social Language Model is additionally fine-trained on a 2k social dataset to detect social text samples.
+Using the [SocRoBERTa-base](https://huggingface.co/ESGBERT/SocRoBERTa-base) model as a starting point, the SocRoBERTa-social Language Model is additionally fine-trained on a 2k social dataset to detect social text samples.
 
 ## How to Get Started With the Model
 You can use the model with a pipeline for text classification:
@@ -22,8 +22,8 @@ You can use the model with a pipeline for text classification:
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
-tokenizer_name = "ESGBERT/SocDistilRoBERTa-social"
-model_name = "ESGBERT/SocDistilRoBERTa-social"
+tokenizer_name = "ESGBERT/SocRoBERTa-social"
+model_name = "ESGBERT/SocRoBERTa-social"
  
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)