Transformers
Safetensors
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use Shijia/flan_biomedidal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shijia/flan_biomedidal with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Shijia/flan_biomedidal") model = AutoModelForSeq2SeqLM.from_pretrained("Shijia/flan_biomedidal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
flan_biomedidal
This model is a fine-tuned version of Shijia/run1 on the sem_eval_2024_task_2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3473
- Accuracy: 0.5
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: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 58 | 0.3479 | 0.5 |
| 0.3671 | 2.0 | 116 | 0.3496 | 0.5 |
| 0.3671 | 3.0 | 174 | 0.3486 | 0.5 |
| 0.37 | 4.0 | 232 | 0.3477 | 0.5 |
| 0.37 | 5.0 | 290 | 0.3473 | 0.5 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Evaluation results
- Accuracy on sem_eval_2024_task_2validation set self-reported0.500