# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="MinhND2301/model_spam2")# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("MinhND2301/model_spam2")
model = AutoModelForSequenceClassification.from_pretrained("MinhND2301/model_spam2")Quick Links
model_spam2
This model is a fine-tuned version of MinhND2301/model_spam on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2344
- Accuracy: 0.9179
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: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|
| No log | 1.0 | 43 | 0.2526 | 0.9091 |
| No log | 2.0 | 86 | 0.2362 | 0.9120 |
| No log | 3.0 | 129 | 0.2344 | 0.9179 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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