Text Classification
Transformers
Safetensors
English
HHEMv2Config
custom_code
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@@ -28,7 +28,7 @@ This model is based on [microsoft/deberta-v3-base](https://huggingface.co/micros
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  * [SummaC Benchmark](https://aclanthology.org/2022.tacl-1.10.pdf) (Test Split) - 0.764 Balanced Accuracy, 0.831 AUC Score
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  * [AnyScale Ranking Test for Hallucinations](https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper) - 86.6 % Accuracy
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- ## Usage
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  The model can be used like this:
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@@ -53,7 +53,7 @@ array([0.61051559, 0.00047493709, 0.99639291, 0.00021221573, 0.99599433, 0.00141
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  ```
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  ## Usage with Transformers AutoModel
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- You can use the model also directly with Transformers library (without SentenceTransformers library):
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
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  * [SummaC Benchmark](https://aclanthology.org/2022.tacl-1.10.pdf) (Test Split) - 0.764 Balanced Accuracy, 0.831 AUC Score
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  * [AnyScale Ranking Test for Hallucinations](https://www.anyscale.com/blog/llama-2-is-about-as-factually-accurate-as-gpt-4-for-summaries-and-is-30x-cheaper) - 86.6 % Accuracy
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+ ## Usage with Sentencer Transformers (Recommended)
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  The model can be used like this:
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  ```
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  ## Usage with Transformers AutoModel
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+ You can use the model also directly with Transformers library (without the SentenceTransformers library):
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  ```python
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  from transformers import AutoTokenizer, AutoModelForSequenceClassification