Instructions to use iTroned/level_c_5_sentiment_tb_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iTroned/level_c_5_sentiment_tb_2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("iTroned/level_c_5_sentiment_tb_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
level_c_5_sentiment_tb_2
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7356
accuracy
: 0.7277
f1
: 0.6986
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-06
- 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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss |
accuracy
|
f1
| |:-------------:|:-----:|:----:|:---------------:|:------------:|:------:| | No log | 1.0 | 122 | 0.8388 | 0.6385 | 0.5802 | | No log | 2.0 | 244 | 0.7833 | 0.6808 | 0.6171 | | No log | 3.0 | 366 | 0.7596 | 0.6948 | 0.6292 | | No log | 4.0 | 488 | 0.7684 | 0.6901 | 0.6251 | | 0.6806 | 5.0 | 610 | 0.7566 | 0.6901 | 0.6318 | | 0.6806 | 6.0 | 732 | 0.7439 | 0.7042 | 0.6419 | | 0.6806 | 7.0 | 854 | 0.7387 | 0.7324 | 0.6969 | | 0.6806 | 8.0 | 976 | 0.7491 | 0.7277 | 0.6917 | | 0.5173 | 9.0 | 1098 | 0.7698 | 0.7277 | 0.6961 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
Model tree for iTroned/level_c_5_sentiment_tb_2
Base model
distilbert/distilbert-base-uncased