Instructions to use ruirui0506/finbert-dividend-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ruirui0506/finbert-dividend-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ruirui0506/finbert-dividend-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ruirui0506/finbert-dividend-sentiment") model = AutoModelForSequenceClassification.from_pretrained("ruirui0506/finbert-dividend-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
finbert-dividend-sentiment
This model is a fine-tuned version of ProsusAI/finbert on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0664
- Accuracy: 0.9890
- F1: 0.9889
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.9772 | 1.0 | 91 | 0.2546 | 0.9116 | 0.9086 |
| 0.1205 | 2.0 | 182 | 0.1469 | 0.9558 | 0.9554 |
| 0.0292 | 3.0 | 273 | 0.0940 | 0.9779 | 0.9779 |
| 0.0205 | 4.0 | 364 | 0.0915 | 0.9779 | 0.9779 |
| 0.0065 | 5.0 | 455 | 0.0887 | 0.9779 | 0.9779 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for ruirui0506/finbert-dividend-sentiment
Base model
ProsusAI/finbert