Instructions to use ullasmrnva/LawBerta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ullasmrnva/LawBerta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ullasmrnva/LawBerta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ullasmrnva/LawBerta") model = AutoModelForSequenceClassification.from_pretrained("ullasmrnva/LawBerta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ullasmrnva/LawBerta: direct link, hf CLI and curl.
- Browser
- Download file 831 Bytes
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https://huggingface.co/ullasmrnva/LawBerta/resolve/main/README.md
- Command line
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hf download hf://ullasmrnva/LawBerta/README.md
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curl -L -o README.md https://huggingface.co/ullasmrnva/LawBerta/resolve/main/README.md
831 Bytes
metadata
tags:
- generated_from_keras_callback
model-index:
- name: attempt
results: []
attempt
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Tokenizers 0.12.1