Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use AmiraliRezaie/university_project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AmiraliRezaie/university_project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AmiraliRezaie/university_project")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AmiraliRezaie/university_project") model = AutoModelForSequenceClassification.from_pretrained("AmiraliRezaie/university_project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
university_project
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9390
- Accuracy: 0.8431
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: 0
- 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 |
|---|---|---|---|---|
| 0.4989 | 1.0 | 1135 | 0.6014 | 0.8109 |
| 0.3582 | 2.0 | 2270 | 0.4748 | 0.8352 |
| 0.2795 | 3.0 | 3405 | 0.7906 | 0.8427 |
| 0.156 | 4.0 | 4540 | 0.8115 | 0.8400 |
| 0.0785 | 5.0 | 5675 | 0.9390 | 0.8431 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for AmiraliRezaie/university_project
Base model
cardiffnlp/twitter-roberta-base