Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use cite-text-analysis/case-analysis-InLegalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cite-text-analysis/case-analysis-InLegalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cite-text-analysis/case-analysis-InLegalBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cite-text-analysis/case-analysis-InLegalBERT") model = AutoModelForSequenceClassification.from_pretrained("cite-text-analysis/case-analysis-InLegalBERT") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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- name: case-analysis-InLegalBERT
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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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should probably proofread and complete it, then remove this comment. -->
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- name: case-analysis-InLegalBERT
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results: []
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---
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## Metrics
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- loss: 1.0434
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- accuracy: 0.8218
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- precision: 0.8145
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- recall: 0.8218
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- precision_macro: 0.6907
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- recall_macro: 0.6533
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- macro_fpr: 0.0897
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- weighted_fpr: 0.0674
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- weighted_specificity: 0.8528
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- macro_specificity: 0.9187
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- weighted_sensitivity: 0.8218
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- macro_sensitivity: 0.6533
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- f1_micro: 0.8218
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- f1_macro: 0.6690
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- f1_weighted: 0.8159
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- runtime: 198.6459
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- samples_per_second: 2.2600
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- steps_per_second: 0.2870
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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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should probably proofread and complete it, then remove this comment. -->
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