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roberta-base-go_emotions

SamLowe

A version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx. These are faster for inference, esp for smaller batch sizes, massively reduce the size of the dependencies required for inference, make inference of the model more multi-platform, and in the case of the quantized version reduce the model file/download size by 75% whilst retaining almost all the accuracy if you only need inference. go_emotions is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label. The model was trained using `AutoModelForSequenceClassification.from_pretrained` with `problem_type="multi_label_classification"` for 3 epochs with a learning rate of 2e-5 and weight decay of 0.01.

Text generationOpen weights

Input Text→ Output Text

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roberta-base-go_emotions-onnx

SamLowe

See more details in the SamLowe/roberta-base-go_emotions model card for the increases possible through selecting label-specific thresholds to maximise F1 scores, or another metric. See more details in the SamLowe/roberta-base-go_emotions model card for the increases possible through selecting label-specific thresholds to maximise F1 scores, or another metric. Optimum library has equivalents (starting `ORT`) for the main Transformers classes, so these models can be used with the familiar constructs. The only extra property needed is `file_name` on the model creation, which in the below example specifies the quantized (INT8) model.

Text analysis

Input Text→ Output Text

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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