Text Classification
Transformers
Safetensors
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Mardiyyah/ds-life-scientist-course-transformer-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mardiyyah/ds-life-scientist-course-transformer-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mardiyyah/ds-life-scientist-course-transformer-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mardiyyah/ds-life-scientist-course-transformer-model") model = AutoModelForSequenceClassification.from_pretrained("Mardiyyah/ds-life-scientist-course-transformer-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ds-life-scientist-course-transformer-model
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the Mardiyyah/pubmed-rct-subset_cleaned dataset. It achieves the following results on the evaluation set:
- Loss: 0.2004
- Accuracy: 0.9313
- F1 Micro: 0.9313
- F1 Macro: 0.8771
- F1 Weighted: 0.9309
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: 32
- seed: 42
- optimizer: Use OptimizerNames.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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|---|
| 0.5702 | 1.0 | 300 | 0.4178 | 0.8538 | 0.8538 | 0.7947 | 0.8528 |
| 0.3403 | 2.0 | 600 | 0.4458 | 0.8562 | 0.8562 | 0.7976 | 0.8536 |
| 0.2524 | 3.0 | 900 | 0.4300 | 0.8712 | 0.8712 | 0.8174 | 0.8700 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.11.0+cu128
- Datasets 3.0.2
- Tokenizers 0.21.4
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