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jaesani
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paraphrase_model

Text Classification
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Safetensors
bert
text-embeddings-inference
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@@ -8,8 +8,8 @@ base_model:
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  - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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  library_name: transformers
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  ---
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- Model Description
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- This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for sentence similarity tasks. It was trained on the mteb/stsbenchmark-sts dataset to evaluate the similarity between sentence pairs.
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  Model Type: Sequence Classification (Regression)
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  Pre-trained Model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
@@ -26,10 +26,11 @@ Weight Decay: 0.01
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  Evaluation
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  The model was evaluated using Pearson correlation on the validation set of the mteb/stsbenchmark-sts dataset. Results indicate how well the model predicts similarity scores between sentence pairs.
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- Usage
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  To use this model for sentence similarity, follow these steps:
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- from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
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  # Load the fine-tuned model
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@@ -67,7 +68,7 @@ If using the model for generating sentence embeddings, you can use the following
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  Domain Specificity: The model is fine-tuned on the mteb/stsbenchmark-sts dataset and may perform differently on other types of text or datasets.
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  Biases: As with any model trained on human language data, it may inherit and reflect biases present in the training data.
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- Future Work
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  Potential improvements include fine-tuning on additional datasets, experimenting with different architectures or hyperparameters, and incorporating additional training techniques to improve performance and robustness.
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  Citation
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  }
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- License
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  This model is licensed under the MIT License. See the LICENSE file for more information.
 
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  - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
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  library_name: transformers
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  ---
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+ # Model Description
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+ This model is a fine-tuned version of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for sentence similarity tasks. It was trained on the mteb/stsbenchmark-sts dataset to evaluate the similarity between sentence pairs.
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  Model Type: Sequence Classification (Regression)
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  Pre-trained Model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
 
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  Evaluation
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  The model was evaluated using Pearson correlation on the validation set of the mteb/stsbenchmark-sts dataset. Results indicate how well the model predicts similarity scores between sentence pairs.
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+ # Usage
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  To use this model for sentence similarity, follow these steps:
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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  # Load the fine-tuned model
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  Domain Specificity: The model is fine-tuned on the mteb/stsbenchmark-sts dataset and may perform differently on other types of text or datasets.
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  Biases: As with any model trained on human language data, it may inherit and reflect biases present in the training data.
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+ # Future Work
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  Potential improvements include fine-tuning on additional datasets, experimenting with different architectures or hyperparameters, and incorporating additional training techniques to improve performance and robustness.
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  Citation
 
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  }
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+ # License
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  This model is licensed under the MIT License. See the LICENSE file for more information.