Token Classification
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use nik548/BioGPT_NCBI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nik548/BioGPT_NCBI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="nik548/BioGPT_NCBI")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("nik548/BioGPT_NCBI") model = AutoModelForTokenClassification.from_pretrained("nik548/BioGPT_NCBI") - Notebooks
- Google Colab
- Kaggle
BioGPT_NCBI
This model is a fine-tuned version of microsoft/biogpt on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1730
- Precision: 0.4537
- Recall: 0.5406
- F1: 0.4933
- Accuracy: 0.9493
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: 4
- eval_batch_size: 4
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2343 | 1.0 | 1358 | 0.1908 | 0.3555 | 0.4292 | 0.3889 | 0.9392 |
| 0.1311 | 2.0 | 2716 | 0.1792 | 0.3994 | 0.5563 | 0.4650 | 0.9429 |
| 0.081 | 3.0 | 4074 | 0.1730 | 0.4537 | 0.5406 | 0.4933 | 0.9493 |
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 nik548/BioGPT_NCBI
Base model
microsoft/biogpt