Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use EMBO/sd-panelization-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/sd-panelization-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EMBO/sd-panelization-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EMBO/sd-panelization-v2") model = AutoModelForTokenClassification.from_pretrained("EMBO/sd-panelization-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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README.md
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model-index:
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- name: sd-panelization-v2
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: source_data_nlp
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type: source_data_nlp
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args: PANELIZATION
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metrics:
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type: precision
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value: 0.9134245120169964
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value: 0.9494824016563147
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value: 0.9311044937736871
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- precision
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- recall
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- f1
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base_model: michiyasunaga/BioLinkBERT-large
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model-index:
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- name: sd-panelization-v2
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results:
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- task:
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type: token-classification
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name: Token Classification
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dataset:
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name: source_data_nlp
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type: source_data_nlp
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args: PANELIZATION
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metrics:
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- type: precision
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value: 0.9134245120169964
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name: Precision
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- type: recall
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value: 0.9494824016563147
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name: Recall
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- type: f1
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value: 0.9311044937736871
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name: F1
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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