Instructions to use CeciliaFu/distilbert-base-uncased-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CeciliaFu/distilbert-base-uncased-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CeciliaFu/distilbert-base-uncased-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CeciliaFu/distilbert-base-uncased-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("CeciliaFu/distilbert-base-uncased-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of somskat/distilbert-base-uncased-finetuned-ner on the None 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Framework versions
- Transformers 4.38.1
- Pytorch 2.2.1+cpu
- Datasets 2.17.1
- Tokenizers 0.15.1
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