Instructions to use leo1234messi/distilbert-finetuned-ner1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leo1234messi/distilbert-finetuned-ner1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="leo1234messi/distilbert-finetuned-ner1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("leo1234messi/distilbert-finetuned-ner1") model = AutoModelForTokenClassification.from_pretrained("leo1234messi/distilbert-finetuned-ner1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-finetuned-ner1
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset.
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 42 | 0.2603 | 0.9172 | 0.9172 | 0.9172 | 0.0476 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3
- Downloads last month
- 7
Model tree for leo1234messi/distilbert-finetuned-ner1
Base model
distilbert/distilbert-base-uncased