Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use allevelly/Movie_Review_Sentiment_Analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allevelly/Movie_Review_Sentiment_Analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="allevelly/Movie_Review_Sentiment_Analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("allevelly/Movie_Review_Sentiment_Analysis") model = AutoModelForSequenceClassification.from_pretrained("allevelly/Movie_Review_Sentiment_Analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Movie_Review_Sentiment_Analysis
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2865
- Accuracy: 0.8986
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2923 | 1.0 | 2500 | 0.2865 | 0.8986 |
| 0.1782 | 2.0 | 5000 | 0.3732 | 0.903 |
| 0.0819 | 3.0 | 7500 | 0.4211 | 0.9164 |
| 0.0434 | 4.0 | 10000 | 0.4677 | 0.9176 |
| 0.0106 | 5.0 | 12500 | 0.5555 | 0.9216 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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