Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("gArthur98/Roberta-classweight-Sentiment-classifier")
model = AutoModelForSequenceClassification.from_pretrained("gArthur98/Roberta-classweight-Sentiment-classifier")Quick Links
Roberta-classweight-Sentiment-classifier
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8656
- F1: 0.6449
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: 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 |
|---|---|---|---|---|
| 0.9827 | 0.5 | 500 | 0.8890 | 0.6395 |
| 0.9185 | 1.0 | 1000 | 0.8708 | 0.6449 |
| 0.8998 | 1.5 | 1500 | 0.8673 | 0.6449 |
| 0.8792 | 2.01 | 2000 | 0.8648 | 0.6449 |
| 0.8877 | 2.51 | 2500 | 0.8656 | 0.6449 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for gArthur98/Roberta-classweight-Sentiment-classifier
Base model
FacebookAI/roberta-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gArthur98/Roberta-classweight-Sentiment-classifier")