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

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

You can select the tagset to use in the output by configuring the model. This models 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": ["multilingual", "uk"], "license": "mit", "tags": ["labse", "ner"]}
transformers pytorch bert labse ner multilingual uk license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us 


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

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

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

More information about the model can be found in the paper (URL and GitHub repository (URL

TAGS
#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us 
