pytorch roberta token-classification dataset:wikiann transformers license:apache-2.0 model-index autotrain_compatible
---
license: apache-2.0
tags:
- token-classification
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-ner-wikiann
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
    metrics:
    - name: Precision
      type: precision
      value: 0.8331921416757433
    - name: Recall
      type: recall
      value: 0.84243586083126
    - name: F1
      type: f1
      value: 0.8377885044416501
    - name: Accuracy
      type: accuracy
      value: 0.91930707459758
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: wikiann
      type: wikiann
      config: en
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9200373733433721
      verified: true
    - name: Precision
      type: precision
      value: 0.9258482820953792
      verified: true
    - name: Recall
      type: recall
      value: 0.9347545055892119
      verified: true
    - name: F1
      type: f1
      value: 0.9302800779500893
      verified: true
    - name: loss
      type: loss
      value: 0.3007512390613556
      verified: true
---

<!-- 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. -->

# distilroberta-base-ner-wikiann

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann dataset.


eval F1-Score: **83,78** 
test F1-Score: **83,76** 


## Model Usage

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-wikiann")
model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann")

nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
example = "My name is Philipp and live in Germany"

nlp(example)

```
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4.9086903597787154e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

It achieves the following results on the evaluation set:
- Loss: 0.3156
- Precision: 0.8332
- Recall: 0.8424
- F1: 0.8378
- Accuracy: 0.9193

It achieves the following results on the test set:
- Loss: 0.3023
- Precision: 0.8301
- Recall: 0.8452
- F1: 0.8376
- Accuracy: 0.92


### Framework versions

- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.2
