# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Propofol/_finetuned-finetuned-localization")
model = AutoModelForSequenceClassification.from_pretrained("Propofol/_finetuned-finetuned-localization", device_map="auto")Quick Links
_finetuned-finetuned-localization
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4382
- Accuracy: 0.436
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.1122 | 1.0 | 2500 | 1.1513 | 0.4287 |
| 1.0035 | 2.0 | 5000 | 1.2395 | 0.4507 |
| 0.7167 | 3.0 | 7500 | 1.4382 | 0.436 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Propofol/_finetuned-finetuned-localization")