Instructions to use Vicman229/distilbert-base-uncased-finetuned-sst-2-english-tuning-amazon-baby-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vicman229/distilbert-base-uncased-finetuned-sst-2-english-tuning-amazon-baby-5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vicman229/distilbert-base-uncased-finetuned-sst-2-english-tuning-amazon-baby-5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vicman229/distilbert-base-uncased-finetuned-sst-2-english-tuning-amazon-baby-5000") model = AutoModelForSequenceClassification.from_pretrained("Vicman229/distilbert-base-uncased-finetuned-sst-2-english-tuning-amazon-baby-5000") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-finetuned-sst-2-english-tuning-amazon-baby-5000
This model is a fine-tuned version of distilbert/distilbert-base-uncased-finetuned-sst-2-english on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0046
- Accuracy: 0.998
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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