metadata
license: mit
language:
- en
- fr
- de
- it
- es
- pt
- pl
- nl
- ru
pipeline_tag: token-classification
inference: false
tags:
- mBERT
- BERT
- generic
- entity-recognition
Model
The multilingual BERT finetunned on an artificially annotated multilingual subset of Oscar dataset. This model provides domain & language independent embedding for Entity Recognition Task.
Usage
Embeddings can be used out of the box or fine-tuned on specific datasets.
Get embeddings:
import torch
import transformers
model = transformers.AutoModel.from_pretrained(
'numind/entity-recognition-general-sota-v1',
output_hidden_states=True,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
'numind/entity-recognition-general-sota-v1',
)
text = [
"NuMind is an AI company based in Paris and USA.",
"See other models from us on https://huggingface.co/numind"
]
encoded_input = tokenizer(
text,
return_tensors='pt',
padding=True,
truncation=True
)
output = model(**encoded_input)
# for better quality
emb = torch.cat(
(output.hidden_states[-1], output.hidden_states[-7]),
dim=2
)
# for better speed
# emb = output.hidden_states[-1]