Pritamdeka
Rank:Average Model Cost: $0.0000
Number of Runs: 21,670
Models by this creator
S-Bluebert-snli-multinli-stsb
S-Bluebert-snli-multinli-stsb
The S-Bluebert-snli-multinli-stsb model is a sentence similarity model trained on the SNLI, MultiNLI, and STSB datasets. It is designed to determine the similarity between two sentences.
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17.3K
Huggingface
BioBERT-mnli-snli-scinli-scitail-mednli-stsb
BioBERT-mnli-snli-scinli-scitail-mednli-stsb
pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI and STSB datasets for providing robust sentence embeddings. Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net Training The model was trained with the parameters: DataLoader: torch.utils.data.dataloader.DataLoader of length 90 with parameters: Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit()-Method: Full Model Architecture Citing & Authors If you use the model kindly cite the following work
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1.3K
Huggingface
BioBert-PubMed200kRCT
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1.1K
Huggingface
S-PubMedBert-MS-MARCO
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1.1K
Huggingface
S-Scibert-snli-multinli-stsb
S-Scibert-snli-multinli-stsb
Platform did not provide a description for this model.
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381
Huggingface
PubMedBERT-MNLI-MedNLI
PubMedBERT-MNLI-MedNLI
PubMedBERT-MNLI-MedNLI This model is a fine-tuned version of PubMedBERT on the MNLI dataset first and then on the MedNLI dataset. It achieves the following results on the evaluation set: Loss: 0.9501 Accuracy: 0.8667 Model description More information needed Intended uses & limitations The model can be used for NLI tasks related to biomedical data and even be adapted to fact-checking tasks. It can be used from the Huggingface pipeline method as follows: The output for the above will be: Training and evaluation data More information needed Training procedure Training hyperparameters The following hyperparameters were used during training: learning_rate: 2e-05 train_batch_size: 32 eval_batch_size: 32 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear num_epochs: 20.0 Training results Framework versions Transformers 4.22.0.dev0 Pytorch 1.12.1+cu113 Datasets 2.4.0 Tokenizers 0.12.1 Citing & Authors If you use the model kindly cite the following work
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192
Huggingface
S-BioBert-snli-multinli-stsb
S-BioBert-snli-multinli-stsb
Platform did not provide a description for this model.
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135
Huggingface
S-Biomed-Roberta-snli-multinli-stsb
S-Biomed-Roberta-snli-multinli-stsb
S-Biomed-Roberta-snli-multinli-stsb This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The base model used is allenai/biomed_roberta_base which has been fine-tuned for sentence similarity. Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net Training The model was trained with the parameters: DataLoader: torch.utils.data.dataloader.DataLoader of length 90 with parameters: Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit()-Method: Full Model Architecture Citing & Authors If you use the model kindly cite the following work
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86
Huggingface
PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb
PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb
pritamdeka/PubMedBERT-mnli-snli-scinli-scitail-mednli-stsb This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI and STSB datasets for providing robust sentence embeddings. Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net Training The model was trained with the parameters: DataLoader: torch.utils.data.dataloader.DataLoader of length 90 with parameters: Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit()-Method: Full Model Architecture Citing & Authors If you use the model kindly cite the following work
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71
Huggingface
SapBERT-mnli-snli-scinli-scitail-mednli-stsb
SapBERT-mnli-snli-scinli-scitail-mednli-stsb
pritamdeka/SapBERT-mnli-snli-scinli-scitail-mednli-stsb This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It has been trained over the SNLI, MNLI, SCINLI, SCITAIL, MEDNLI and STSB datasets for providing robust sentence embeddings. Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net Training The model was trained with the parameters: DataLoader: torch.utils.data.dataloader.DataLoader of length 90 with parameters: Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit()-Method: Full Model Architecture Citing & Authors If you use the model kindly cite the following work
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65
Huggingface