metadata
license: mit
language:
- en
pipeline_tag: token-classification
inference: false
tags:
- token-classification
- entity-recognition
- generic
- feature-extraction
- foundation-model
SOTA Entity Recognition V1 foundation model by NuMind 🔥
This model provides the best embedding for the Entity Recognition task.
Checkout other models by NuMind:
- SOTA multilingual Entity Recognition foundation model: link
- SOTA Sentiment Analysis foundation model: English, Multilingual
About
Roberta-base fine-tuned on an artificially annotated subset of C4.
Results:
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]
Contact
Sergei Bogdanov: [email protected]