Text Classification
Transformers
PyTorch
TensorBoard
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use scales-okn/ontology-motion-to-certify-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scales-okn/ontology-motion-to-certify-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scales-okn/ontology-motion-to-certify-class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("scales-okn/ontology-motion-to-certify-class") model = AutoModelForSequenceClassification.from_pretrained("scales-okn/ontology-motion-to-certify-class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Document GPL-3.0 public release
Browse files
README.md
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model-index:
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- name: ontology-motion-to-certify-class
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results: []
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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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- Pytorch 1.11.0+cu113
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- Datasets 1.18.3
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- Tokenizers 0.11.0
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model-index:
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- name: ontology-motion-to-certify-class
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results: []
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license: gpl-3.0
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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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- Pytorch 1.11.0+cu113
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- Datasets 1.18.3
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- Tokenizers 0.11.0
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## Public release information
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This model is released by the SCALES Open Knowledge Network under the GNU General
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Public License v3.0. It is derived from `scales-okn/docket-language-model` and is
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intended for research and development involving legal-document classification or
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information extraction. It is not legal advice.
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The organization has reviewed the release decision and confirmed that the model's
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training data and resulting weights are legally and ethically releasable. Users are
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responsible for evaluating accuracy, bias, privacy, and fitness for their own use.
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The repository includes PyTorch `.bin` artifacts. Hugging Face's server-side security
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scan reported no file issues before publication. As with any serialized model
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artifact, load it only with maintained libraries and in an appropriately isolated
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environment.
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