
# rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of otvet.mail.ru questions and answers. The raw dataset can be found [here](https://www.kaggle.com/datasets/atleast6characterss/otvetmailru-full). Beware that the data contains a good portion of toxic language, so the answers can be unpredictable.

Jupyter notebook with an example of how to inference this model can be found in the [repository](https://github.com/NeuralPushkin/MailRu_Q-A)
{"language": ["ru"], "tags": ["PyTorch", "Transformers"]}
transformers pytorch safetensors gpt2 text-generation PyTorch Transformers ru autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of URL questions and answers. The raw dataset can be found here. Beware that the data contains a good portion of toxic language, so the answers can be unpredictable.

Jupyter notebook with an example of how to inference this model can be found in the repository
# rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of URL questions and answers. The raw dataset can be found here. Beware that the data contains a good portion of toxic language, so the answers can be unpredictable.

Jupyter notebook with an example of how to inference this model can be found in the repository
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of URL questions and answers. The raw dataset can be found here. Beware that the data contains a good portion of toxic language, so the answers can be unpredictable.

Jupyter notebook with an example of how to inference this model can be found in the repository
passage: TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #PyTorch #Transformers #ru #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# rugpt3large\_mailqa
Model was finetuned with sequence length 1024 for 516000 steps on a dataset of URL questions and answers. The raw dataset can be found here. Beware that the data contains a good portion of toxic language, so the answers can be unpredictable.

Jupyter notebook with an example of how to inference this model can be found in the repository