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--- |
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license: mit |
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language: |
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- en |
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- fr |
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- de |
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- it |
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- es |
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- pt |
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- pl |
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- nl |
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- ru |
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pipeline_tag: token-classification |
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inference: false |
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tags: |
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- mBERT |
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- BERT |
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- generic |
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- entity-recognition |
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--- |
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## Model |
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The [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) finetunned on an artificially annotated multilingual subset of [Oscar dataset](https://huggingface.co/datasets/oscar-corpus/OSCAR-2201). This model provides domain & language independent embedding for Entity Recognition Task. |
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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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``` |
