AI model creator details for nikcheerla

nooks-amd-detection-realtime

The nooks-amd-detection-realtime model is a sentence-transformers model that maps sentences and paragraphs to a 768 dimensional vector space. It can be used for tasks like clustering and semantic search. The model can be used either with the sentence-transformers library or the HuggingFace Transformers library. It was trained using the CosineSimilarityLoss and the full model architecture and training parameters are available. For evaluating the model, the Sentence Embeddings Benchmark can be used. The model was developed and trained by an unknown author.

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15.5K

Huggingface

nooks-amd-detection

nikcheerla

{MODEL_NAME} 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. 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 480 with parameters: Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit()-Method: Full Model Architecture Citing & Authors

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16

Huggingface

nooks-amd-detection-full

nikcheerla

{MODEL_NAME} 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. 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 6048 with parameters: Loss: sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss Parameters of the fit()-Method: Full Model Architecture Citing & Authors

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15

Huggingface

nooks-amd-detection-v2-full

nikcheerla

The "nooks-amd-detection-v2-full" model is a sentence-transformers model that maps sentences and paragraphs to a 768 dimensional dense vector space. It can be used for tasks like clustering or semantic search. The model can be used with the sentence-transformers library or with the HuggingFace Transformers library. It was trained with the parameters specified and has been evaluated using the Sentence Embeddings Benchmark. The model architecture and information on how to cite the model is provided.

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7

Huggingface

nooks-amd-detection-v4

nikcheerla

nikcheerla/nooks-amd-detection-v4 This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: Fine-tuning a Sentence Transformer with contrastive learning. Training a classification head with features from the fine-tuned Sentence Transformer. Usage To use this model for inference, first install the SetFit library: You can then run inference as follows: BibTeX entry and citation info

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3

Huggingface

nooks-amd-detection-v3

nikcheerla

nikcheerla/nooks-amd-detection-v3 This is a SetFit model that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: Fine-tuning a Sentence Transformer with contrastive learning. Training a classification head with features from the fine-tuned Sentence Transformer. Usage To use this model for inference, first install the SetFit library: You can then run inference as follows: BibTeX entry and citation info

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3

Huggingface

nooks-amd-detection-full-v3

nikcheerla

Platform did not provide a description for this model.

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3

Huggingface

nooks-amd-detection-full-v2