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german-sentiment-bert

Text Classification Transformers PyTorch TensorFlow JAX Safetensors German bert sentiment Inference Endpoints

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german-sentiment-bert / eval_results_germansentiment.txt

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	germansentiment = {'acc': 0.9644997518799863, 'f1': 0.9644997518799863}
	loss = 0.14042662027189262

	\| file \| precisionMicro \| recallMicro \| fscoreMicro \| precisionMacro \| recallMacro \| fscoreMacro \|
	\|:---------------------------\|-----------------:\|--------------:\|--------------:\|-----------------:\|--------------:\|--------------:\|
	\| holidaycheck \| 0.9568 \| 0.9568 \| 0.9568 \| 0.6363 \| 0.6385 \| 0.6373 \|
	\| scare \| 0.9418 \| 0.9418 \| 0.9418 \| 0.6223 \| 0.6224 \| 0.6223 \|
	\| filmstarts \| 0.9021 \| 0.9021 \| 0.9021 \| 0.5925 \| 0.5914 \| 0.5918 \|
	\| germeval \| 0.7536 \| 0.7536 \| 0.7536 \| 0.5136 \| 0.5155 \| 0.5139 \|
	\| PotTS \| 0.6780 \| 0.6780 \| 0.6780 \| 0.6432 \| 0.6659 \| 0.6508 \|
	\| emotions \| 0.9649 \| 0.9649 \| 0.9649 \| 0.6606 \| 0.7717 \| 0.6828 \|
	\| sb10k \| 0.7376 \| 0.7376 \| 0.7376 \| 0.6161 \| 0.5970 \| 0.6027 \|
	\| leipzig-deu-wikipedia-2016 \| 0.9967 \| 0.9967 \| 0.9967 \| 0.3322 \| 0.3333 \| 0.3328 \|
	\| all \| 0.9639 \| 0.9639 \| 0.9639 \| 0.9639 \| 0.9640 \| 0.9639 \|
	\| sum \| 7.8953 \| 7.8953 \| 7.8953 \| 5.5808 \| 5.6998 \| 5.5984 \|