Instructions to use eliasbe/IceBERT-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eliasbe/IceBERT-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="eliasbe/IceBERT-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("eliasbe/IceBERT-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("eliasbe/IceBERT-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Error:"model-index[0].results" is required
IceBERT-finetuned-ner
This model is a fine-tuned version of eliasbe/IceBERT-finetuned-ner on the mim_gold_ner 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 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
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