Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use raminass/british with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raminass/british with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raminass/british")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raminass/british") model = AutoModelForSequenceClassification.from_pretrained("raminass/british") - Notebooks
- Google Colab
- Kaggle
british
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: 0.5705
- Accuracy: 0.7368
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 |
|---|---|---|---|---|
| No log | 1.0 | 16 | 0.5992 | 0.6842 |
| No log | 2.0 | 32 | 0.5983 | 0.6667 |
| No log | 3.0 | 48 | 0.5679 | 0.7193 |
| No log | 4.0 | 64 | 0.5713 | 0.7368 |
| No log | 5.0 | 80 | 0.5705 | 0.7368 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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