
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on CroSloEngual (https://huggingface.co/EMBEDDIA/crosloengual-bert) and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SSJ500k (LOC, MISC, ORG, PER)

PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date

You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.

More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
{"language": ["hr", "sl", "en", "multilingual"], "license": "mit", "tags": ["CroSloEngual", "ner"]}
transformers pytorch bert CroSloEngual ner hr sl en multilingual license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us 


This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on CroSloEngual (URL and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SSJ500k (LOC, MISC, ORG, PER)

PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date

You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.

More information about the model can be found in the paper (URL and GitHub repository (URL
TAGS
#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us 
