Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use KABANDA18/Movie_Review_Roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KABANDA18/Movie_Review_Roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KABANDA18/Movie_Review_Roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KABANDA18/Movie_Review_Roberta") model = AutoModelForSequenceClassification.from_pretrained("KABANDA18/Movie_Review_Roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Movie_Review_Roberta
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2690
- Accuracy: 0.9382
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.2761 | 1.0 | 623 | 0.2936 | 0.9191 |
| 0.1606 | 2.0 | 1246 | 0.2460 | 0.9392 |
| 0.1001 | 3.0 | 1869 | 0.2690 | 0.9382 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
- Downloads last month
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Model tree for KABANDA18/Movie_Review_Roberta
Base model
FacebookAI/roberta-base