Text Classification
Transformers
TensorBoard
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use KAITANY/finetuned-roberta-base-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KAITANY/finetuned-roberta-base-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KAITANY/finetuned-roberta-base-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KAITANY/finetuned-roberta-base-sentiment") model = AutoModelForSequenceClassification.from_pretrained("KAITANY/finetuned-roberta-base-sentiment") - Notebooks
- Google Colab
- Kaggle
finetuned-roberta-base-sentiment
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-sentiment-latest on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5188
- F1: 0.8046
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: 10
- eval_batch_size: 10
- seed: 42
- 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 | F1 |
|---|---|---|---|---|
| 0.4094 | 1.0 | 800 | 0.5188 | 0.8046 |
| 0.3489 | 2.0 | 1600 | 0.5808 | 0.7902 |
| 0.2556 | 3.0 | 2400 | 0.7118 | 0.8017 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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