---
base_model: samrawal/bert-base-uncased_clinical-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [samrawal/bert-base-uncased_clinical-ner](https://huggingface.co/samrawal/bert-base-uncased_clinical-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4144
- Precision: 0.5379
- Recall: 0.6266
- F1: 0.5789
- Accuracy: 0.8522

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 188  | 0.4360          | 0.5166    | 0.5835 | 0.5480 | 0.8411   |
| No log        | 2.0   | 376  | 0.4066          | 0.5414    | 0.6322 | 0.5833 | 0.8521   |
| 0.4136        | 3.0   | 564  | 0.4144          | 0.5379    | 0.6266 | 0.5789 | 0.8522   |


### Framework versions

- Transformers 4.34.0
- Pytorch 1.12.1
- Datasets 2.14.5
- Tokenizers 0.14.1