Ready-to-run GGUF packages of **ZipVoice-Distill** (k2-fsa), a zero-shot voice-cloning TTS with a TTSZipformer flow-matching backbone and a Vocos 24 kHz vocoder, packaged for the audio.cpp engine. Each file is fully self-contained: flow-matching model + bundled Vocos vocoder + embedded text-frontend sidecars (`tokens.txt`, `model.json`, zh tables, jieba dictionaries). Output: 24 kHz mono WAV. **Q8_0 is the recommended download.** Weights are stored as int8 blocks (one F16 scale per 32 weights); activations stay F32 and the kernels dequantize each block on the fly — no full-model dequantization at load, no int8 arithmetic.
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View details ↗AfroXLMR-base was created by MLM adaptation of XLM-R-base model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Naija, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high-resource languages (Arabic, French, and English). language| XLM-R-miniLM| XLM-R-base |XLM-R-large| afro-xlmr-base | afro-xlmr-small | afro-xlmr-mini -|-|-|-|-|-|- amh |69.5|70.6|76.2|76.1|70.1|69.7 hau |74.5|89.5|90.5|91.2|91.4|87.7 ibo |81.9|84.8|84.1|87.4|86.6|83.5 kin |68.6|73.3|73.8|78.0|77.5|74.1 lug |64.7|79.7|81.6|82.9|83.2|77.4 luo |11.7|74.9|73.6|75.1|75.4|17.5 pcm |83.2|87.3|89.0|89.6|89.0|85.5 swa |86.3|87.4|89.4|88.6|88.7|86.0 wol |51.7|63.9|67.9|67.4|65.9|59.0 yor |72.0|78.3|78.9|82.1|81.3|75.1
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View details ↗AfroXLMR-large was created by MLM adaptation of XLM-R-large model on 17 African languages (Afrikaans, Amharic, Hausa, Igbo, Malagasy, Chichewa, Oromo, Nigerian-Pidgin, Kinyarwanda, Kirundi, Shona, Somali, Sesotho, Swahili, isiXhosa, Yoruba, and isiZulu) covering the major African language families and 3 high-resource languages (Arabic, French, and English). language| XLM-R-miniLM| XLM-R-base |XLM-R-large | afro-xlmr-large | afro-xlmr-base | afro-xlmr-small | afro-xlmr-mini -|-|-|-|-|-|-|- amh |69.5|70.6|76.2|79.7|76.1|70.1|69.7 hau |74.5|89.5|90.5|91.4|91.2|91.4|87.7 ibo |81.9|84.8|84.1|87.7|87.4|86.6|83.5 kin |68.6|73.3|73.8|79.1|78.0|77.5|74.1 lug |64.7|79.7|81.6|86.7|82.9|83.2|77.4 luo |11.7|74.9|73.6|78.1|75.1|75.4|17.5 pcm |83.2|87.3|89.0|91.0|89.6|89.0|85.5 swa |86.3|87.4|89.4|90.4|88.6|88.7|86.0 wol |51.7|63.9|67.9|69.6|67.4|65.9|59.0 yor |72.0|78.3|78.9|85.2|82.1|81.3|75.1 avg |66.4|79.0|80.5|83.9|81.8|80.9|71.6
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View details ↗Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **bert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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View details ↗Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **distilbert-base-multilingual-cased-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned Distiled BERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *distilbert-base-multilingual-cased* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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View details ↗Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **xlm-roberta-base-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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View details ↗Hugging Face's logo --- language: - ar - de - en - es - fr - it - lv - nl - pt - zh - multilingual --- **xlm-roberta-large-ner-hrl** is a **Named Entity Recognition** model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned XLM-RoBERTa large model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER). Specifically, this model is a *xlm-roberta-large* model that was fine-tuned on an aggregation of 10 high-resourced languages You can use this model with Transformers *pipeline* for NER. This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. The training data for the 10 languages are from:
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