---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- Ben10x/MedMentions-NER
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-medmentions
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: Ben10x/MedMentions-NER
      type: Ben10x/MedMentions-NER
    metrics:
    - name: Precision
      type: precision
      value: 0.5820728291316527
    - name: Recall
      type: recall
      value: 0.6344207955338451
    - name: F1
      type: f1
      value: 0.6071204975165909
    - name: Accuracy
      type: accuracy
      value: 0.8688595400463357
---

<!-- 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. -->

# bert-base-medmentions

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Ben10x/MedMentions-NER dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5156
- Precision: 0.5821
- Recall: 0.6344
- F1: 0.6071
- Accuracy: 0.8689

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15.0
- label_smoothing_factor: 0.2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.5686        | 1.0   | 2911  | 1.5440          | 0.5246    | 0.6123 | 0.5650 | 0.8550   |
| 1.4792        | 2.0   | 5822  | 1.5156          | 0.5821    | 0.6344 | 0.6071 | 0.8689   |
| 1.4111        | 3.0   | 8733  | 1.5191          | 0.5865    | 0.6494 | 0.6163 | 0.8714   |
| 1.356         | 4.0   | 11644 | 1.5293          | 0.6236    | 0.6403 | 0.6318 | 0.8777   |
| 1.3182        | 5.0   | 14555 | 1.5433          | 0.6283    | 0.6426 | 0.6354 | 0.8789   |
| 1.2919        | 6.0   | 17466 | 1.5671          | 0.6242    | 0.6628 | 0.6429 | 0.8794   |
| 1.2743        | 7.0   | 20377 | 1.5697          | 0.6356    | 0.6574 | 0.6463 | 0.8809   |
| 1.2633        | 8.0   | 23288 | 1.5806          | 0.6364    | 0.6699 | 0.6528 | 0.8813   |
| 1.2542        | 9.0   | 26199 | 1.5942          | 0.6278    | 0.6734 | 0.6498 | 0.8808   |
| 1.2457        | 10.0  | 29110 | 1.6076          | 0.6372    | 0.6634 | 0.6500 | 0.8814   |
| 1.2398        | 11.0  | 32021 | 1.6077          | 0.6414    | 0.6696 | 0.6552 | 0.8835   |
| 1.2377        | 12.0  | 34932 | 1.6135          | 0.6478    | 0.6759 | 0.6615 | 0.8847   |
| 1.2349        | 13.0  | 37843 | 1.6195          | 0.6433    | 0.6756 | 0.6590 | 0.8839   |
| 1.2328        | 14.0  | 40754 | 1.6228          | 0.6462    | 0.6726 | 0.6592 | 0.8845   |
| 1.231         | 15.0  | 43665 | 1.6247          | 0.6473    | 0.6735 | 0.6601 | 0.8847   |


### Framework versions

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