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---
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
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