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---
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
|