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# distilroberta-base-ner-wikiann-conll2003-3-class

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. 

O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).

eval F1-Score: **96,25** (merged dataset)   
test F1-Score: **92,41** (merged dataset) 



## Model Usage

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

tokenizer = AutoTokenizer.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")

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: 0.0520
- Precision: 0.9625
- Recall: 0.9667
- F1: 0.9646
- Accuracy: 0.9914

It achieves the following results on the test set:
- Loss: 0.141
- Precision: 0.917
- Recall: 0.9313
- F1: 0.9241
- Accuracy: 0.9807


### Framework versions

- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["token-classification"], "datasets": ["wikiann-conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilroberta-base-ner-wikiann-conll2003-3-class", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "wikiann-conll2003", "type": "wikiann-conll2003"}, "metrics": [{"type": "precision", "value": 0.9624757386241104, "name": "Precision"}, {"type": "recall", "value": 0.9667497021553124, "name": "Recall"}, {"type": "f1", "value": 0.964607986167396, "name": "F1"}, {"type": "accuracy", "value": 0.9913626461292995, "name": "Accuracy"}]}]}]}
transformers pytorch roberta token-classification dataset:wikiann-conll2003 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# distilroberta-base-ner-wikiann-conll2003-3-class

This model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. 

O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).

eval F1-Score: 96,25 (merged dataset)   
test F1-Score: 92,41 (merged dataset) 



## 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: 0.0520
- Precision: 0.9625
- Recall: 0.9667
- F1: 0.9646
- Accuracy: 0.9914

It achieves the following results on the test set:
- Loss: 0.141
- Precision: 0.917
- Recall: 0.9313
- F1: 0.9241
- Accuracy: 0.9807


### Framework versions

- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.3

# distilroberta-base-ner-wikiann-conll2003-3-class

This model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. 

O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).

eval F1-Score: 96,25 (merged dataset)   
test F1-Score: 92,41 (merged dataset) ## 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: 0.0520
- Precision: 0.9625
- Recall: 0.9667
- F1: 0.9646
- Accuracy: 0.9914

It achieves the following results on the test set:
- Loss: 0.141
- Precision: 0.917
- Recall: 0.9313
- F1: 0.9241
- Accuracy: 0.9807 ### Framework versions

- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # distilroberta-base-ner-wikiann-conll2003-3-class

This model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. 

O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).

eval F1-Score: 96,25 (merged dataset)   
test F1-Score: 92,41 (merged dataset) ## 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: 0.0520
- Precision: 0.9625
- Recall: 0.9667
- F1: 0.9646
- Accuracy: 0.9914

It achieves the following results on the test set:
- Loss: 0.141
- Precision: 0.917
- Recall: 0.9313
- F1: 0.9241
- Accuracy: 0.9807 ### Framework versions

- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #roberta #token-classification #dataset-wikiann-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# distilroberta-base-ner-wikiann-conll2003-3-class

This model is a fine-tuned version of distilroberta-base on the wikiann and conll2003 dataset. It consists out of the classes of wikiann. 

O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).

eval F1-Score: 96,25 (merged dataset)   
test F1-Score: 92,41 (merged dataset)## 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: 0.0520
- Precision: 0.9625
- Recall: 0.9667
- F1: 0.9646
- Accuracy: 0.9914

It achieves the following results on the test set:
- Loss: 0.141
- Precision: 0.917
- Recall: 0.9313
- F1: 0.9241
- Accuracy: 0.9807### Framework versions

- Transformers 4.6.1
- Pytorch 1.8.1+cu101
- Datasets 1.6.2
- Tokenizers 0.10.3