Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Junr-syl/sentiments_analysis_Roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Junr-syl/sentiments_analysis_Roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Junr-syl/sentiments_analysis_Roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Junr-syl/sentiments_analysis_Roberta") model = AutoModelForSequenceClassification.from_pretrained("Junr-syl/sentiments_analysis_Roberta") - Notebooks
- Google Colab
- Kaggle
sentiments_analysis_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: 0.6669
- Accuracy: 0.7365
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7879 | 1.0 | 500 | 0.7282 | 0.7095 |
| 0.6982 | 2.0 | 1000 | 0.6883 | 0.719 |
| 0.651 | 3.0 | 1500 | 0.6618 | 0.7315 |
| 0.611 | 4.0 | 2000 | 0.6669 | 0.7365 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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