Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use prithviraj-maurya/legalbench_answer_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithviraj-maurya/legalbench_answer_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prithviraj-maurya/legalbench_answer_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prithviraj-maurya/legalbench_answer_classification") model = AutoModelForSequenceClassification.from_pretrained("prithviraj-maurya/legalbench_answer_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aca33cd9778f1033f6a369b98c108c69370a915ac952fda7fb42f325c3b7e292
- Size of remote file:
- 4.73 kB
- SHA256:
- 8125f5f6e9972a46f91b06b81e8330d1a1f48ad00931036c77d9e983a7b8a8c4
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