---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conllpp
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: conllpp_NER
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conllpp
      type: conllpp
      config: conllpp
      split: test
      args: conllpp
    metrics:
    - name: Precision
      type: precision
      value: 0.8583545377438507
    - name: Recall
      type: recall
      value: 0.8874079270431428
    - name: F1
      type: f1
      value: 0.8726394757264812
    - name: Accuracy
      type: accuracy
      value: 0.9747427663835371
---

<!-- 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. -->

# conllpp_NER

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the conllpp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0877
- Precision: 0.8584
- Recall: 0.8874
- F1: 0.8726
- Accuracy: 0.9747

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 439  | 0.0943          | 0.8399    | 0.8700 | 0.8547 | 0.9716   |
| 0.2003        | 2.0   | 878  | 0.0877          | 0.8584    | 0.8874 | 0.8726 | 0.9747   |


### Framework versions

- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0