diff --git a/model_cards/aliosm/ComVE-distilgpt2/README.md b/model_cards/aliosm/ComVE-distilgpt2/README.md
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+---
+language: "en"
+tags:
+- exbert
+- commonsense
+- semeval2020
+- comve
+license: "mit"
+datasets:
+- ComVE
+metrics:
+- bleu
+widget:
+- text: "Chicken can swim in water. <|continue|>"
+---
+
+# ComVE-distilgpt2
+
+## Model description
+
+Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) 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.
+
+#### How to use
+
+You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) 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.
+
+#### Limitations and bias
+
+The model biased to negate the entered sentence usually instead of producing a factual reason.
+
+## Training data
+
+The model is initialized from the [distilgpt2](https://github.com/huggingface/transformers/blob/master/model_cards/distilgpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) 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
+
+```bibtex
+@article{fadel2020justers,
+ title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
+ author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
+ year={2020}
+}
+```
+
+
+
+
diff --git a/model_cards/aliosm/ComVE-gpt2-large/README.md b/model_cards/aliosm/ComVE-gpt2-large/README.md
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+---
+language: "en"
+tags:
+- gpt2
+- exbert
+- commonsense
+- semeval2020
+- comve
+license: "mit"
+datasets:
+- https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation
+metrics:
+- bleu
+widget:
+- text: "Chicken can swim in water. <|continue|>"
+---
+
+# ComVE-gpt2-large
+
+## Model description
+
+Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) 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.
+
+#### How to use
+
+You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) 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.
+
+#### Limitations and bias
+
+The model biased to negate the entered sentence usually instead of producing a factual reason.
+
+## Training data
+
+The model is initialized from the [gpt2-large](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) 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 `<|conteniue|>` 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 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{fadel2020justers,
+ title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
+ author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
+ year={2020}
+}
+```
+
+
+
+
diff --git a/model_cards/aliosm/ComVE-gpt2-medium/README.md b/model_cards/aliosm/ComVE-gpt2-medium/README.md
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index 0000000000..fb4571c19b
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+---
+language: "en"
+tags:
+- gpt2
+- exbert
+- commonsense
+- semeval2020
+- comve
+license: "mit"
+datasets:
+- ComVE
+metrics:
+- bleu
+widget:
+- text: "Chicken can swim in water. <|continue|>"
+---
+
+# ComVE-gpt2-medium
+
+## Model description
+
+Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) 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.
+
+#### How to use
+
+You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) 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.
+
+#### Limitations and bias
+
+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](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) 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:
+| Against Commonsense Statement | Generated Reason |
+|:-----------------------------------------------------:|:--------------------------------------------:|
+| Chicken can swim in water. | Chicken can't swim. |
+| shoes can fly | Shoes are not able to fly. |
+| Chocolate can be used to make a coffee pot | Chocolate is not used to make coffee pots. |
+| you can also buy tickets online with an identity card | You can't buy tickets with an identity card. |
+| a ball is square and can roll | A ball is round and cannot roll. |
+| You can use detergent to dye your hair. | Detergent is used to wash clothes. |
+| you can eat mercury | mercury is poisonous |
+| A gardener can follow a suspect | gardener is not a police officer |
+| cars can float in the ocean just like a boat | Cars are too heavy to float in the ocean. |
+| I am going to work so I can lose money. | Working is not a way to lose money. |
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{fadel2020justers,
+ title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
+ author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
+ year={2020}
+}
+```
+
+
+
+
diff --git a/model_cards/aliosm/ComVE-gpt2/README.md b/model_cards/aliosm/ComVE-gpt2/README.md
new file mode 100644
index 0000000000..75acc61ab1
--- /dev/null
+++ b/model_cards/aliosm/ComVE-gpt2/README.md
@@ -0,0 +1,67 @@
+---
+language: "en"
+tags:
+- exbert
+- commonsense
+- semeval2020
+- comve
+license: "mit"
+datasets:
+- ComVE
+metrics:
+- bleu
+widget:
+- text: "Chicken can swim in water. <|continue|>"
+---
+
+# ComVE-gpt2
+
+## Model description
+
+Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) 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.
+
+#### How to use
+
+You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) 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.
+
+#### Limitations and bias
+
+The model biased to negate the entered sentence usually instead of producing a factual reason.
+
+## Training data
+
+The model is initialized from the [gpt2](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) 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
+
+```bibtex
+@article{fadel2020justers,
+ title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
+ author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
+ year={2020}
+}
+```
+
+
+
+