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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: token-classification |
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inference: false |
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tags: |
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- token-classification |
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- entity-recognition |
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- generic |
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- feature-extraction |
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- foundation-model |
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--- |
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# SOTA Entity Recognition V1 foundation model by NuMind 🔥 |
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This model provides the best embedding for the Entity Recognition task. |
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**Checkout other models by NuMind:** |
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* SOTA multilingual Entity Recognition foundation model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1) |
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* SOTA Sentiment Analysis foundation model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1) |
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## About |
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[Roberta-base](https://huggingface.co/roberta-base) fine-tuned on an artificially annotated subset of [C4](https://huggingface.co/datasets/c4). |
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**Results:** |
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## Usage |
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Embeddings can be used out of the box or fine-tuned on specific datasets. |
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Get embeddings: |
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```python |
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import torch |
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import transformers |
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model = transformers.AutoModel.from_pretrained( |
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'numind/entity-recognition-general-sota-v1', |
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output_hidden_states=True |
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) |
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tokenizer = transformers.AutoTokenizer.from_pretrained( |
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'numind/entity-recognition-general-sota-v1' |
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) |
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text = [ |
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"NuMind is an AI company based in Paris and USA.", |
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"See other models from us on https://huggingface.co/numind" |
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] |
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encoded_input = tokenizer( |
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text, |
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return_tensors='pt', |
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padding=True, |
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truncation=True |
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) |
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output = model(**encoded_input) |
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# for better quality |
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emb = torch.cat( |
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(output.hidden_states[-1], output.hidden_states[-7]), |
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dim=2 |
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) |
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# for better speed |
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# emb = output.hidden_states[-1] |
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``` |
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## Contact |
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Sergei Bogdanov: [email protected] |
