Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use nolo-test/paul-test-classification-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nolo-test/paul-test-classification-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nolo-test/paul-test-classification-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nolo-test/paul-test-classification-roberta") model = AutoModelForSequenceClassification.from_pretrained("nolo-test/paul-test-classification-roberta") - Notebooks
- Google Colab
- Kaggle
paul-test-classification-roberta
This model is a fine-tuned version of FacebookAI/roberta-large-mnli on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0
- Accuracy: 1.0
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: 64
- eval_batch_size: 64
- 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 |
|---|---|---|---|---|
| 44912717332.48 | 1.0 | 25 | 0.0 | 1.0 |
| 0.0 | 2.0 | 50 | 0.0 | 1.0 |
| 0.0 | 3.0 | 75 | 0.0 | 1.0 |
| 0.0 | 4.0 | 100 | 0.0 | 1.0 |
| 0.0 | 5.0 | 125 | 0.0 | 1.0 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for nolo-test/paul-test-classification-roberta
Base model
FacebookAI/roberta-large-mnli