Xtreme S Xlsr 300m Minds14 ONNX

--- language: - all license: apache-2.0 tags: - minds14 - google/xtreme_s - generated_from_trainer datasets: - google/xtreme_s metrics: - f1 - accuracy model-index: - name: xtreme_s_xlsr_300m_minds14 results: [] library_name: transformers.js base_model: - anton-l/xtreme_s_xlsr_300m_minds14 pipeline_tag: audio-classification --- # xtreme_s_xlsr_300m_minds14 (ONNX) This is an ONNX version of [anton-l/xtreme_s_xlsr_300m_minds14](https://huggingface.co/anton-l/xtreme_s_xlsr_300m_minds14). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx). ## Usage with Transformers.js See the pipeline documentation for `audio-classification`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.AudioClassificationPipeline --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xtreme_s_xlsr_300m_minds14 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.ALL dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9033 - Accuracy Cs-cz: 0.9164 - Accuracy De-de: 0.9477 - Accuracy En-au: 0.9235 - Accuracy En-gb: 0.9324 - Accuracy En-us: 0.9326 - Accuracy Es-es: 0.9177 - Accuracy Fr-fr: 0.9444 - Accuracy It-it: 0.9167 - Accuracy Ko-kr: 0.8649 - Accuracy Nl-nl: 0.9450 - Accuracy Pl-pl: 0.9146 - Accuracy Pt-pt: 0.8940 - Accuracy Ru-ru: 0.8667 - Accuracy Zh-cn: 0.7291 - F1: 0.9015 - F1 Cs-cz: 0.9154 - F1 De-de: 0.9467 - F1 En-au: 0.9199 - F1 En-gb: 0.9334 - F1 En-us: 0.9308 - F1 Es-es: 0.9158 - F1 Fr-fr: 0.9436 - F1 It-it: 0.9135 - F1 Ko-kr: 0.8642 - F1 Nl-nl: 0.9440 - F1 Pl-pl: 0.9159 - F1 Pt-pt: 0.8883 - F1 Ru-ru: 0.8646 - F1 Zh-cn: 0.7249 - Loss: 0.4119 - Loss Cs-cz: 0.3790 - Loss De-de: 0.2649 - Loss En-au: 0.3459 - Loss En-gb: 0.2853 - Loss En-us: 0.2203 - Loss Es-es: 0.2731 - Loss Fr-fr: 0.1909 - Loss It-it: 0.3520 - Loss Ko-kr: 0.5431 - Loss Nl-nl: 0.2515 - Loss Pl-pl: 0.4113 - Loss Pt-pt: 0.4798 - Loss Ru-ru: 0.6470 - Loss Zh-cn: 1.1216 - Predict Samples: 4086 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Mor...