Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use DaisyQue/finetuning-sentiment-model-300-samples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaisyQue/finetuning-sentiment-model-300-samples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DaisyQue/finetuning-sentiment-model-300-samples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DaisyQue/finetuning-sentiment-model-300-samples") model = AutoModelForSequenceClassification.from_pretrained("DaisyQue/finetuning-sentiment-model-300-samples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: distilbert-base-uncased | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: finetuning-sentiment-model-300-samples | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # finetuning-sentiment-model-300-samples | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.9609 | |
| - Accuracy: 0.765 | |
| - F1: 0.7648 | |
| ## 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: 128 | |
| - eval_batch_size: 64 | |
| - seed: 42 | |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 5 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | |
| | 0.2642 | 1.0 | 55 | 0.6175 | 0.783 | 0.7826 | | |
| | 0.2391 | 2.0 | 110 | 0.6746 | 0.761 | 0.7607 | | |
| | 0.1272 | 3.0 | 165 | 0.8233 | 0.764 | 0.7636 | | |
| | 0.0772 | 4.0 | 220 | 0.9219 | 0.76 | 0.7594 | | |
| | 0.0647 | 5.0 | 275 | 0.9609 | 0.765 | 0.7648 | | |
| ### Framework versions | |
| - Transformers 4.46.2 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 | |