---
library_name: transformers
license: mit
base_model: EleutherAI/gpt-neo-1.3B
tags:
- generated_from_trainer
datasets:
- Ben10x/MedMentions-MTI881-NER
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gpt-medmentions
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Ben10x/MedMentions-MTI881-NER
      type: Ben10x/MedMentions-MTI881-NER
    metrics:
    - name: Precision
      type: precision
      value: 0.4453316069630269
    - name: Recall
      type: recall
      value: 0.5247499576199356
    - name: F1
      type: f1
      value: 0.48178988326848243
    - name: Accuracy
      type: accuracy
      value: 0.8454107464662687
---

<!-- 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 the Ben10x/MedMentions-MTI881-NER dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5111
- Precision: 0.4453
- Recall: 0.5247
- F1: 0.4818
- Accuracy: 0.8454

## 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
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5307        | 1.0   | 5850  | 0.5369          | 0.4129    | 0.4711 | 0.4401 | 0.8341   |
| 0.3585        | 2.0   | 11700 | 0.5111          | 0.4453    | 0.5247 | 0.4818 | 0.8454   |
| 0.1758        | 3.0   | 17550 | 0.6349          | 0.4718    | 0.4900 | 0.4807 | 0.8497   |
| 0.0751        | 4.0   | 23400 | 0.9264          | 0.4628    | 0.5208 | 0.4901 | 0.8497   |
| 0.0387        | 5.0   | 29250 | 1.0903          | 0.4758    | 0.5181 | 0.4960 | 0.8518   |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1