Token Classification
Transformers
Safetensors
Portuguese
bert
feature-extraction
coreference-resolution
meeting-minutes
portuguese
spanbert
administrative-documents
Instructions to use anonymous270126/SpanBERT-Coreference-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anonymous270126/SpanBERT-Coreference-pt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="anonymous270126/SpanBERT-Coreference-pt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("anonymous270126/SpanBERT-Coreference-pt") model = AutoModel.from_pretrained("anonymous270126/SpanBERT-Coreference-pt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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license: other
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license_name: custom-agplv3-dual-license
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license_link: https://huggingface.co/
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tags:
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- coreference-resolution
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- meeting-minutes
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## How It Works
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The model uses a two-stage pipeline. First, entity spans are identified by a NER model ([
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**INPUT:**
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```
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license: other
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license_name: custom-agplv3-dual-license
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license_link: https://huggingface.co/anonymous270126/SpanBERT-Coreference-pt/blob/main/LICENSE
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tags:
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- coreference-resolution
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- meeting-minutes
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## How It Works
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The model uses a two-stage pipeline. First, entity spans are identified by a NER model ([anonymous270126/SpanBERT-Coreference-pt](https://huggingface.co/anonymous270126/SpanBERT-Coreference-pt)). Then, the coreference model groups spans that refer to the same entity, assigning consistent IDs throughout the document.
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**INPUT:**
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```
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