Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/sentiment_analysis_model")
model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/sentiment_analysis_model")Quick Links
sentiment_analysis_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7543
- Accuracy: 0.8483
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 150 | 0.4045 | 0.8317 |
| No log | 2.0 | 300 | 0.4403 | 0.83 |
| No log | 3.0 | 450 | 0.5234 | 0.8325 |
| 0.3116 | 4.0 | 600 | 0.5604 | 0.8367 |
| 0.3116 | 5.0 | 750 | 0.6089 | 0.8425 |
| 0.3116 | 6.0 | 900 | 0.6792 | 0.85 |
| 0.0814 | 7.0 | 1050 | 0.7147 | 0.8508 |
| 0.0814 | 8.0 | 1200 | 0.7421 | 0.8517 |
| 0.0814 | 9.0 | 1350 | 0.7794 | 0.845 |
| 0.0302 | 10.0 | 1500 | 0.7543 | 0.8483 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.1.2
- Datasets 2.18.0
- 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="Mahmoud8/sentiment_analysis_model")