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

# distilbert-base-uncased

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0007
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.0025        | 0.7273 | 500  | 0.0007          | 1.0       | 1.0    | 1.0 | 1.0      |
| 0.0011        | 1.4545 | 1000 | 0.0003          | 1.0       | 1.0    | 1.0 | 1.0      |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1