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---
library_name: transformers
license: mit
base_model: EleutherAI/gpt-neo-1.3B
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gpt-medmentions
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-medmentions
This model is a fine-tuned version of [EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4512
- Precision: 0.4110
- Recall: 0.5052
- F1: 0.4533
- Accuracy: 0.4334
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- total_eval_batch_size: 16
- 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: 5.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2514 | 1.0 | 2917 | 1.2192 | 0.4016 | 0.3853 | 0.3933 | 0.4191 |
| 1.0825 | 2.0 | 5834 | 1.1892 | 0.4234 | 0.6373 | 0.5088 | 0.4324 |
| 0.9093 | 3.0 | 8751 | 1.2419 | 0.4105 | 0.4848 | 0.4446 | 0.4308 |
| 0.7938 | 4.0 | 11668 | 1.3655 | 0.3774 | 0.3369 | 0.3560 | 0.4301 |
| 0.746 | 5.0 | 14585 | 1.4512 | 0.4110 | 0.5052 | 0.4533 | 0.4334 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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