Kevinscaria
Rank:Average Model Cost: $0.0000
Number of Runs: 7,996
Models by this creator
joint_tk-instruct-base-def-pos-neg-neut-combined
joint_tk-instruct-base-def-pos-neg-neut-combined
The joint_tk-instruct-base-def-pos-neg-neut-combined model is finetuned for the Joint Task, specifically for Aspect Based Sentiment Analysis (ABSA). It is trained on the SemEval 2014 benchmark dataset, which includes reviews from laptops and restaurant domains along with aspect term and polarity labels. The model is the current state-of-the-art for the Joint Task. The architecture and prompts used in the fine-tuning process can be found in the official implementation of the paper "InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis".
$-/run
7.0K
Huggingface
atsc_tk-instruct-base-def-pos-neg-neut-combined
atsc_tk-instruct-base-def-pos-neg-neut-combined
atsc_tk-instruct-base-def-pos-neg-neut-combined This model is finetuned for the Aspect Term Sentiment Classification (ATSC) subtask. The finetuning was carried out by adding prompts of the form: definition + 2 positive examples + 2 negative examples + 2 neutral examples The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from both laptops and restaurants domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here. For the ATSC subtask, this model has a competitive performance with the current SOTA. Training data InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels. BibTeX entry and citation info If you use this model in your work, please cite the following paper:
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464
Huggingface
ate_tk-instruct-base-def-pos-neg-neut-combined
ate_tk-instruct-base-def-pos-neg-neut-combined
This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form: The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from both laptops and restaurants domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here. For the ATE subtask, this model is the current SOTA. InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels. If you use this model in your work, please cite the following paper:
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437
Huggingface
ate_tk-instruct-base-def-pos-laptops
ate_tk-instruct-base-def-pos-laptops
ate_tk-instruct-base-def-pos-laptops This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form: definition + 2 positive examples The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from the laptops domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here. For the ATE subtask, this model is the current SOTA. Training data InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels. BibTeX entry and citation info If you use this model in your work, please cite the following paper:
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26
Huggingface
ate_tk-instruct-base-def-pos-neg-neut-laptops
ate_tk-instruct-base-def-pos-neg-neut-laptops
Platform did not provide a description for this model.
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21
Huggingface
atsc_tk-instruct-base-def-pos-laptops
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20
Huggingface
joint_tk-instruct-base-def-pos-neg-neut-restaurants
joint_tk-instruct-base-def-pos-neg-neut-restaurants
joint_tk-instruct-base-def-pos-neg-neut-restaurants This model is finetuned for the Joint Task. The finetuning was carried out by adding prompts of the form: definition + 2 positive examples + 2 negative examples + 2 neutral examples The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from the restaurants domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here. For the Joint Task, this model is the current SOTA. Training data InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels. BibTeX entry and citation info If you use this model in your work, please cite the following paper:
$-/run
14
Huggingface
joint_tk-instruct-base-def-pos-combined
joint_tk-instruct-base-def-pos-combined
joint_tk-instruct-base-def-pos-combined This model is finetuned for the Joint Task. The finetuning was carried out by adding prompts of the form: definition + 2 positive examples + 2 negative examples + 2 neutral examples The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from the laptops and restaurants domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here. For the Joint Task, this model is the current SOTA. Training data InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels. BibTeX entry and citation info If you use this model in your work, please cite the following paper:
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14
Huggingface
ate_tk-instruct-base-def-pos-restaurants
ate_tk-instruct-base-def-pos-restaurants
Platform did not provide a description for this model.
$-/run
14
Huggingface
ate_tk-instruct-base-def-pos-neg-neut-restaurants
ate_tk-instruct-base-def-pos-neg-neut-restaurants
This model is finetuned for the Aspect Term Extraction (ATE) subtask. The finetuning was carried out by adding prompts of the form: The prompt is prepended onto each input review. It is important to note that this model output was finetuned on samples from the restaurants domains. The code for the official implementation of the paper InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis can be found here. For the ATE subtask, this model is the current SOTA. InstructABSA models are trained on the benchmark dataset for Aspect Based Sentiment Analysis tasks viz. SemEval 2014. This dataset consists of reviews from laptops and restaurant domains and their corresponding aspect term and polarity labels. If you use this model in your work, please cite the following paper:
$-/run
14
Huggingface