Instructions to use Maoger/TAC_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maoger/TAC_dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Maoger/TAC_dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Maoger/TAC_dataset") model = AutoModelForTokenClassification.from_pretrained("Maoger/TAC_dataset") - Notebooks
- Google Colab
- Kaggle
TAC_dataset
This model is a fine-tuned version of microsoft/biogpt on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3005
- Precision: 0.125
- Recall: 0.0018
- F1: 0.0036
- Accuracy: 0.9191
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: 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: 3
Training results
Framework versions
- Transformers 4.51.2
- Pytorch 2.1.0+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1
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Model tree for Maoger/TAC_dataset
Base model
microsoft/biogpt