Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("raminass/SCOTUS_AI_V17_FINAL")
model = AutoModelForSequenceClassification.from_pretrained("raminass/SCOTUS_AI_V17_FINAL")Quick Links
SCOTUS_AI_V17_FINAL
This model is a fine-tuned version of raminass/scotus-v10 on the None dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.0629
- eval_accuracy: 0.9837
- eval_runtime: 159.8848
- eval_samples_per_second: 272.346
- eval_steps_per_second: 17.025
- epoch: 4.0
- step: 10888
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:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raminass/SCOTUS_AI_V17_FINAL")