Instructions to use sxandie/san_BERT1-newData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sxandie/san_BERT1-newData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sxandie/san_BERT1-newData")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sxandie/san_BERT1-newData") model = AutoModelForTokenClassification.from_pretrained("sxandie/san_BERT1-newData") - Notebooks
- Google Colab
- Kaggle
test_3
This model is a fine-tuned version of deepset/gbert-base on an unknown dataset. It achieves the following results on the evaluation set:
- precision: 0.0058
- recall: 0.0176
- f1: 0.0088
- accuracy: 0.0300
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:
- num_train_epochs: 5
- train_batch_size: 16
- eval_batch_size: 32
- learning_rate: 2e-05
- weight_decay_rate: 0.01
- num_warmup_steps: 0
- fp16: True
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.2.2
- Tokenizers 0.13.3
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