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afro-xlmr-large

Davlan

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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요금 미확인멀티모달 및 채널 요금 별도 확인

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afro-xlmr-large-75L

Davlan

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

기본 정보 등록 · 상세 정보 보완 예정

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afro-xlmr-large-76L

Davlan

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

afro-xlmr-mini

Davlan

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

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bert-base-multilingual-cased-finetuned-amharic

Davlan

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

기본 정보 등록 · 상세 정보 보완 예정

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

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bert-base-multilingual-cased-ner-hrl

Davlan

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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요금 미확인멀티모달 및 채널 요금 별도 확인

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distilbert-base-multilingual-cased-ner-hrl

Davlan

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:

번역 대기 중 · 원문 표시

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

xlm-roberta-base-ner-hrl

Davlan

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:

번역 대기 중 · 원문 표시

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

xlm-roberta-base-wikiann-ner

Davlan

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

비교 추가출처 / 문서 ↗

xlm-roberta-large-ner-hrl

Davlan

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:

번역 대기 중 · 원문 표시

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인