Token Classification
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use wins1502/MLMA_biogpt_ADR_1412 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wins1502/MLMA_biogpt_ADR_1412 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="wins1502/MLMA_biogpt_ADR_1412")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wins1502/MLMA_biogpt_ADR_1412") model = AutoModelForTokenClassification.from_pretrained("wins1502/MLMA_biogpt_ADR_1412") - Notebooks
- Google Colab
- Kaggle
MLMA_biogpt_ADR_1412
This model is a fine-tuned version of wins1502/MLMA_biogpt_tuned_1412 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1387
- Precision: 0.5611
- Recall: 0.5980
- F1: 0.5790
- Accuracy: 0.9528
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 |
|---|---|---|---|---|---|---|---|
| 0.2211 | 1.0 | 590 | 0.1710 | 0.4371 | 0.5052 | 0.4687 | 0.9372 |
| 0.146 | 2.0 | 1180 | 0.1456 | 0.5459 | 0.5009 | 0.5225 | 0.9498 |
| 0.1114 | 3.0 | 1770 | 0.1387 | 0.5611 | 0.5980 | 0.5790 | 0.9528 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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