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# 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

{"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"}]}]}]}
transformers pytorch roberta token-classification dataset:wikiann license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# distilroberta-base-ner-wikiann

This model is a fine-tuned version of distilroberta-base on the wikiann dataset.


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


## Model Usage


## 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

# distilroberta-base-ner-wikiann

This model is a fine-tuned version of distilroberta-base on the wikiann dataset.


eval F1-Score: 83,78 
test F1-Score: 83,76 ## Model Usage ## 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
TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # distilroberta-base-ner-wikiann

This model is a fine-tuned version of distilroberta-base on the wikiann dataset.


eval F1-Score: 83,78 
test F1-Score: 83,76 ## Model Usage ## 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
passage: TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# distilroberta-base-ner-wikiann

This model is a fine-tuned version of distilroberta-base on the wikiann dataset.


eval F1-Score: 83,78 
test F1-Score: 83,76## Model Usage## 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