# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Youssef320/OUTPUT")
model = AutoModelForSequenceClassification.from_pretrained("Youssef320/OUTPUT", device_map="auto")Quick Links
OUTPUT
This model is a fine-tuned version of Youssef320/LSTM-finetuned-50label-15epoch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.9546
- Top 1 Macro F1 Score: 0.1199
- Top 1 Weighted F1score: 0.1730
- Top 3 Macro F1 Score: 0.2702
- Top3 3 Weighted F1 Score : 0.3774
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: 0.0002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Top 1 Macro F1 Score | Top 1 Weighted F1score | Top 3 Macro F1 Score | Top3 3 Weighted F1 Score |
|---|---|---|---|---|---|---|---|
| 3.0865 | 0.53 | 32 | 2.9546 | 0.1199 | 0.1730 | 0.2702 | 0.3774 |
Framework versions
- Transformers 4.28.1
- Pytorch 1.12.1+cu102
- Datasets 2.6.1
- 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="Youssef320/OUTPUT")