Instructions to use vinodfnu/my_finetuned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vinodfnu/my_finetuned_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vinodfnu/my_finetuned_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vinodfnu/my_finetuned_model") model = AutoModelForSequenceClassification.from_pretrained("vinodfnu/my_finetuned_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
my_finetuned_model
This model is a fine-tuned version of vinodfnu/my-fine-tuned-finbert-model on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0038
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: 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: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
Framework versions
- Transformers 4.56.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for vinodfnu/my_finetuned_model
Base model
vinodfnu/my-fine-tuned-finbert-model