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@@ -8,13 +8,13 @@ tags:
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  - social
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  ---
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- # Model Card for SocDistilRoBERTa-social
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  ## Model Description
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- 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.
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- 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.
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  ## How to Get Started With the Model
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  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:
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  ```python
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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- tokenizer_name = "ESGBERT/SocDistilRoBERTa-social"
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- model_name = "ESGBERT/SocDistilRoBERTa-social"
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  model = AutoModelForSequenceClassification.from_pretrained(model_name)
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  tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)
 
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  - social
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  ---
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+ # Model Card for SocRoBERTa-social
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  ## Model Description
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+ 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.
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+ 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.
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  ## How to Get Started With the Model
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  You can use the model with a pipeline for text classification:
 
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  ```python
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  from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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+ tokenizer_name = "ESGBERT/SocRoBERTa-social"
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+ model_name = "ESGBERT/SocRoBERTa-social"
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  model = AutoModelForSequenceClassification.from_pretrained(model_name)
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  tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512)