---
license: apache-2.0
tags:
- token-classification
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilroberta-base-ner-wikiann
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: wikiann
      type: wikiann
    metrics:
    - type: precision
      value: 0.8331921416757433
      name: Precision
    - type: recall
      value: 0.84243586083126
      name: Recall
    - type: f1
      value: 0.8377885044416501
      name: F1
    - type: accuracy
      value: 0.91930707459758
      name: Accuracy
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: wikiann
      type: wikiann
      config: en
      split: test
    metrics:
    - type: accuracy
      value: 0.9200373733433721
      name: Accuracy
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGFmMTNkZDYwMDllNjE5ZTVjYzYwYTQyMDFjYzNkYTkxZmVmOTNkOTFlOTU4MmM2MmFlMWQzMTcwZGViOTA3ZCIsInZlcnNpb24iOjF9.pOwPcBmA7XJdq9QgCNoCivTsu0WfsCnvRtzObDrqhFtrO2PjLNf9tmlQeahGcBGFo6yIHvhndBYwf__lN-4nBg
    - type: precision
      value: 0.9258482820953792
      name: Precision
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzFhNGJlMzk0N2JmYmU3YjAxZjJjNGFjZjZjOTJhODc3MjQyODMzYzE2Y2Y4NWQ4YThhMjg3NWI1MGRmODczMiIsInZlcnNpb24iOjF9.eVTQJqXeGY0XZaGURXBrT8sjMl7O_SxuFB4NS7C6jbpr46MMZdusvzkmndOIrGjReB2vB3sAmpcT0hydpqRkDg
    - type: recall
      value: 0.9347545055892119
      name: Recall
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2Y5ZGIzM2JlOWNjZGUzOWU5MGIwOTFiODM4NmU3NGQ3ZmUxYzM4ZmYxNjIwOTE0ZWFiYWJhMzk4NDg4ZjI3MSIsInZlcnNpb24iOjF9.tzl3gTEDFuj7kpGsERkQzXfh7B0Qwao31VcXKF1rSvf3ulVgXsU-vTB2oZiGr3w5AySr_80J0pIpSpvGzfhNAQ
    - type: f1
      value: 0.9302800779500893
      name: F1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjY5MDM2ZWQ1MzJmNDFhMGFmZmQ1MzM0NmJmOTVmYTM1OWZmNzc4YWI4ZWUwMTFlMTQ5MTJmYWRhNmVmZTUyZCIsInZlcnNpb24iOjF9.zMUq4ZGLfu0eQF7lHNkaf6LByypIevygVGLpBA3jW80OUy5VeZDK7d6q0RV_N4SO5gTkLEjoDvSqLDcaw-9VBw
    - type: loss
      value: 0.3007512390613556
      name: loss
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzI5YmIxODFkN2NkYzJkZDgyZTc4MDhlMDkyMzM3NWFiZWQ1MmUzMDA1MGYyM2RlNzVlNTIwNDcwNTFmNjYwMSIsInZlcnNpb24iOjF9.D8vx5YhoNHY4CdRXEt3rL95odR2kZJ1e_c34HD28xX9YeWKIjjt4E0FSz6Xw4ufJd9UlCnQ_u4VPFTYI-RXlCQ
---

<!-- 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