AI model creator details for aliosm

ComVE-distilgpt2

ComVE-distilgpt2 Model description Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective. The model is able to generate a reason why a given natural language statement is against commonsense. Intended uses & limitations You can use the raw model for text generation to generate reasons why natural language statements are against commonsense. You can use this model directly to generate reasons why the given statement is against commonsense using generate.sh script. Note: make sure that you are using version 2.4.1 of transformers package. Newer versions has some issue in text generation and the model repeats the last token generated again and again. The model biased to negate the entered sentence usually instead of producing a factual reason. Training data The model is initialized from the distilgpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons. Training procedure Each natural language statement that against commonsense is concatenated with its reference reason with <|continue|> as a separator, then the model finetuned using CLM objective. The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 15 epochs, 128 maximum sequence length and 64 batch size. Eval results The model achieved 13.7582/13.8026 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset. BibTeX entry and citation info

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4.6K

Huggingface

sha3bor-rhyme-detector-arabertv02-base

aliosm

Platform did not provide a description for this model.

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26

Huggingface

sha3bor-generator-aragpt2-medium

aliosm

No description available.

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24

Huggingface

ComVE-gpt2

aliosm

ComVE-gpt2 Model description Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective. The model is able to generate a reason why a given natural language statement is against commonsense. Intended uses & limitations You can use the raw model for text generation to generate reasons why natural language statements are against commonsense. You can use this model directly to generate reasons why the given statement is against commonsense using generate.sh script. Note: make sure that you are using version 2.4.1 of transformers package. Newer versions has some issue in text generation and the model repeats the last token generated again and again. The model biased to negate the entered sentence usually instead of producing a factual reason. Training data The model is initialized from the gpt2 model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons. Training procedure Each natural language statement that against commonsense is concatenated with its reference reason with <|continue|> as a separator, then the model finetuned using CLM objective. The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size. Eval results The model achieved 14.0547/13.6534 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset. BibTeX entry and citation info

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21

Huggingface

ComVE-gpt2-medium

aliosm

ComVE-gpt2-medium Model description Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in SemEval2020 Task4 using a causal language modeling (CLM) objective. The model is able to generate a reason why a given natural language statement is against commonsense. Intended uses & limitations You can use the raw model for text generation to generate reasons why natural language statements are against commonsense. You can use this model directly to generate reasons why the given statement is against commonsense using generate.sh script. Note: make sure that you are using version 2.4.1 of transformers package. Newer versions has some issue in text generation and the model repeats the last token generated again and again. The model biased to negate the entered sentence usually instead of producing a factual reason. Training data The model is initialized from the gpt2-medium model and finetuned using ComVE dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons. Training procedure Each natural language statement that against commonsense is concatenated with its reference reason with <|continue|> as a separator, then the model finetuned using CLM objective. The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size. Eval results The model achieved fifth place with 16.7153/16.1187 BLEU scores and third place with 1.94 Human Evaluation score on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset. These are some examples generated by the model: BibTeX entry and citation info

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20

Huggingface

sha3bor-footer-51-arabertv02-base

aliosm

Platform did not provide a description for this model.

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17

Huggingface

sha3bor-general-diacritizer-canine-c