
## Twitter-roBERTa-base fine-tuned using masked language modelling
This is a RoBERTa-base model finetuned (domain adaptation) on ~2M tweets from Jin 2009 (sentiment140).
This is the first step of a two steps approach to finetune for sentiment analysis (ULMFit)
This model is suitable for English.

Main charachetistics:
- pretrained model and tokenizer: distillroberta-base
- no cleaning/processing applied to the data

Reference Paper: [ULMFit](https://arxiv.org/abs/1801.06146).
Reference dataset: [Sentiment140](https://www.kaggle.com/datasets/kazanova/sentiment140?resource=download)
Git Repo: TBD
Labels: 0 -> Negative; 1 -> Positive
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["distillroberta-base", "twitter"], "datasets": ["andrea-t94/TwitterSentiment140"], "metrics": ["perplexity"], "pipeline_tag": "fill-mask"}
transformers pytorch roberta fill-mask distillroberta-base twitter en dataset:andrea-t94/TwitterSentiment140 arxiv:1801.06146 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #distillroberta-base #twitter #en #dataset-andrea-t94/TwitterSentiment140 #arxiv-1801.06146 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


## Twitter-roBERTa-base fine-tuned using masked language modelling
This is a RoBERTa-base model finetuned (domain adaptation) on ~2M tweets from Jin 2009 (sentiment140).
This is the first step of a two steps approach to finetune for sentiment analysis (ULMFit)
This model is suitable for English.

Main charachetistics:
- pretrained model and tokenizer: distillroberta-base
- no cleaning/processing applied to the data

Reference Paper: ULMFit.
Reference dataset: Sentiment140
Git Repo: TBD
Labels: 0 -> Negative; 1 -> Positive
## Twitter-roBERTa-base fine-tuned using masked language modelling
This is a RoBERTa-base model finetuned (domain adaptation) on ~2M tweets from Jin 2009 (sentiment140).
This is the first step of a two steps approach to finetune for sentiment analysis (ULMFit)
This model is suitable for English.

Main charachetistics:
- pretrained model and tokenizer: distillroberta-base
- no cleaning/processing applied to the data

Reference Paper: ULMFit.
Reference dataset: Sentiment140
Git Repo: TBD
Labels: 0 -> Negative; 1 -> Positive
TAGS
#transformers #pytorch #roberta #fill-mask #distillroberta-base #twitter #en #dataset-andrea-t94/TwitterSentiment140 #arxiv-1801.06146 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## Twitter-roBERTa-base fine-tuned using masked language modelling
This is a RoBERTa-base model finetuned (domain adaptation) on ~2M tweets from Jin 2009 (sentiment140).
This is the first step of a two steps approach to finetune for sentiment analysis (ULMFit)
This model is suitable for English.

Main charachetistics:
- pretrained model and tokenizer: distillroberta-base
- no cleaning/processing applied to the data

Reference Paper: ULMFit.
Reference dataset: Sentiment140
Git Repo: TBD
Labels: 0 -> Negative; 1 -> Positive
TAGS
#transformers #pytorch #roberta #fill-mask #distillroberta-base #twitter #en #dataset-andrea-t94/TwitterSentiment140 #arxiv-1801.06146 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## Twitter-roBERTa-base fine-tuned using masked language modelling
This is a RoBERTa-base model finetuned (domain adaptation) on ~2M tweets from Jin 2009 (sentiment140).
This is the first step of a two steps approach to finetune for sentiment analysis (ULMFit)
This model is suitable for English.

Main charachetistics:
- pretrained model and tokenizer: distillroberta-base
- no cleaning/processing applied to the data

Reference Paper: ULMFit.
Reference dataset: Sentiment140
Git Repo: TBD
Labels: 0 -> Negative; 1 -> Positive