NuNER-v0.1 / README.md
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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]