---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: finetuned_bert_pos_model
  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. -->

# finetuned_bert_pos_model

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1559
- Precision: 0.9454
- Recall: 0.9426
- F1: 0.9440
- Accuracy: 0.9521

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 123  | 0.1823          | 0.9362    | 0.9322 | 0.9342 | 0.9438   |
| No log        | 2.0   | 246  | 0.1700          | 0.9409    | 0.9381 | 0.9395 | 0.9482   |
| No log        | 3.0   | 369  | 0.1618          | 0.9431    | 0.9403 | 0.9417 | 0.9501   |
| No log        | 4.0   | 492  | 0.1564          | 0.9448    | 0.9418 | 0.9433 | 0.9516   |
| 0.1554        | 5.0   | 615  | 0.1559          | 0.9454    | 0.9426 | 0.9440 | 0.9521   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2