---
license: apache-2.0
base_model: dslim/distilbert-NER
tags:
- generated_from_trainer
datasets:
- conll2012_ontonotesv5
metrics:
- accuracy
- f1
model-index:
- name: distilbert-NER-finetuned
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2012_ontonotesv5
      type: conll2012_ontonotesv5
      config: english_v4
      split: validation
      args: english_v4
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8738244514106583
    - name: F1
      type: f1
      value: 0.4990403071017275
---

<!-- 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-NER-finetuned

This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the conll2012_ontonotesv5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4666
- Accuracy: 0.8738
- F1: 0.4990

## 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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8992        | 1.0   | 61   | 0.6227          | 0.8404   | 0.4295 |
| 0.5484        | 2.0   | 122  | 0.5143          | 0.8631   | 0.4784 |
| 0.4243        | 3.0   | 183  | 0.4757          | 0.8710   | 0.4985 |
| 0.3599        | 4.0   | 244  | 0.4666          | 0.8738   | 0.4990 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1