Token Classification
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use aprilyan/biogpt-custom-ADR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aprilyan/biogpt-custom-ADR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="aprilyan/biogpt-custom-ADR")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("aprilyan/biogpt-custom-ADR") model = AutoModelForTokenClassification.from_pretrained("aprilyan/biogpt-custom-ADR") - Notebooks
- Google Colab
- Kaggle
biogpt-custom-ADR
This model is a fine-tuned version of microsoft/biogpt on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2017
- Precision: 0.4480
- Recall: 0.3880
- F1: 0.4158
- Accuracy: 0.9303
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2905 | 1.0 | 596 | 0.2391 | 0.2939 | 0.3406 | 0.3156 | 0.9140 |
| 0.2044 | 2.0 | 1192 | 0.2040 | 0.4877 | 0.2923 | 0.3655 | 0.9293 |
| 0.1723 | 3.0 | 1788 | 0.2017 | 0.4480 | 0.3880 | 0.4158 | 0.9303 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for aprilyan/biogpt-custom-ADR
Base model
microsoft/biogpt