Token Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
Instructions to use CelinaLopGa/distilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CelinaLopGa/distilbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CelinaLopGa/distilbert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CelinaLopGa/distilbert-base-uncased") model = AutoModelForTokenClassification.from_pretrained("CelinaLopGa/distilbert-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased
This model was trained from scratch on the hipe2020 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5372
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8783
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 21 | 0.6196 | 0.0 | 0.0 | 0.0 | 0.8783 |
| No log | 2.0 | 42 | 0.5704 | 0.0 | 0.0 | 0.0 | 0.8783 |
| No log | 3.0 | 63 | 0.5372 | 0.0 | 0.0 | 0.0 | 0.8783 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Evaluation results
- Precision on hipe2020validation set self-reported0.000
- Recall on hipe2020validation set self-reported0.000
- F1 on hipe2020validation set self-reported0.000
- Accuracy on hipe2020validation set self-reported0.878