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_17")
model = AutoModelForSequenceClassification.from_pretrained("raminass/SCOTUS_AI_17")Quick Links
SCOTUS_AI_17
This model is a fine-tuned version of raminass/scotus-v10 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0723
- Accuracy: 0.8263
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2805 | 1.0 | 3188 | 0.6360 | 0.8303 |
| 0.1147 | 2.0 | 6376 | 0.8285 | 0.8230 |
| 0.053 | 3.0 | 9564 | 1.0048 | 0.8208 |
| 0.0228 | 4.0 | 12752 | 1.0853 | 0.8183 |
| 0.0143 | 5.0 | 15940 | 1.0723 | 0.8263 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raminass/SCOTUS_AI_17")