# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DenilsenAxel/nlp-text-classification")
model = AutoModelForSequenceClassification.from_pretrained("DenilsenAxel/nlp-text-classification")Quick Links
test_trainer
This model is a fine-tuned version of bert-base-uncased on the amazon_us_reviews dataset. It achieves the following results on the evaluation set:
- Loss: 0.9348
- Accuracy: 0.7441
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: 5e-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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6471 | 1.0 | 7500 | 0.6596 | 0.7376 |
| 0.5235 | 2.0 | 15000 | 0.6997 | 0.7423 |
| 0.3955 | 3.0 | 22500 | 0.9348 | 0.7441 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
- Downloads last month
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Dataset used to train DenilsenAxel/nlp-text-classification
Evaluation results
- Accuracy on amazon_us_reviewsself-reported0.744
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DenilsenAxel/nlp-text-classification")