Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use incredible45/News-Sentimental-model-Buy-Neutral-Sell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use incredible45/News-Sentimental-model-Buy-Neutral-Sell with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="incredible45/News-Sentimental-model-Buy-Neutral-Sell")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("incredible45/News-Sentimental-model-Buy-Neutral-Sell") model = AutoModelForSequenceClassification.from_pretrained("incredible45/News-Sentimental-model-Buy-Neutral-Sell") - Notebooks
- Google Colab
- Kaggle
News-Sentimental-model-Buy-Neutral-Sell
This model is a fine-tuned version of ProsusAI/finbert on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0538
- Accuracy: 0.4431
- F1: 0.4431
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- Transformers 4.30.2
- Pytorch 2.1.0.dev20230523+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
- Downloads last month
- 15
Model tree for incredible45/News-Sentimental-model-Buy-Neutral-Sell
Base model
ProsusAI/finbert