Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use utkarshbelkhede/finbert-sec-10K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utkarshbelkhede/finbert-sec-10K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="utkarshbelkhede/finbert-sec-10K")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("utkarshbelkhede/finbert-sec-10K") model = AutoModelForSequenceClassification.from_pretrained("utkarshbelkhede/finbert-sec-10K") - Notebooks
- Google Colab
- Kaggle
finbert
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2184
- Accuracy: 0.8947
- F1: 0.7370
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: 16
- eval_batch_size: 64
- 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 | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 20 | 0.3729 | 0.8647 | 0.4637 |
| No log | 2.0 | 40 | 0.2622 | 0.8647 | 0.5134 |
| No log | 3.0 | 60 | 0.2184 | 0.8947 | 0.7370 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Downloads last month
- 3