
# Info

This is a fine-tuned model on the NER task. The original model is Turku NLP's [bert-base-finnish-uncased-v1](https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1), and the fine-tuning dataset is Turku NLP's [turku_ner_corpus](https://huggingface.co/datasets/turku_ner_corpus/). 

The model is released under Apache 2.0.

Please mention the training dataset if you use this model:

```bibtex
@inproceedings{luoma-etal-2020-broad,
    title = "A Broad-coverage Corpus for {F}innish Named Entity Recognition",
    author = {Luoma, Jouni and Oinonen, Miika and Pyyk{\"o}nen, Maria and Laippala, Veronika and Pyysalo, Sampo},
    booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
    year = "2020",
    url = "https://www.aclweb.org/anthology/2020.lrec-1.567",
    pages = "4615--4624",
}
```

# Validation Metrics

- Loss: 0.075
- Accuracy: 0.982
- Precision: 0.879
- Recall: 0.868
- F1: 0.873

# Test Metrics

### Overall Metrics

- Accuracy: 0.986
- Precision: 0.857
- Recall: 0.872
- F1: 0.864

### Per-entity metrics

```json
{
    "DATE": {
        "precision": 0.925,
        "recall": 0.9736842105263158,
        "f1": 0.9487179487179489,
        "number": "114"
    },
    "EVENT": {
        "precision": 0.3,
        "recall": 0.42857142857142855,
        "f1": 0.3529411764705882,
        "number": "7"
    },
    "LOC": {
        "precision": 0.9057239057239057,
        "recall": 0.9372822299651568,
        "f1": 0.9212328767123287,
        "number": "287"
    },
    "ORG": {
        "precision": 0.8274111675126904,
        "recall": 0.7836538461538461,
        "f1": 0.8049382716049382,
        "number": "208"
    },
    "PER": {
        "precision": 0.88,
        "recall": 0.9225806451612903,
        "f1": 0.9007874015748031,
        "number": "310"
    },
    "PRO": {
        "precision": 0.6081081081081081,
        "recall": 0.569620253164557,
        "f1": 0.5882352941176471,
        "number": "79"
    }
}
```

## Usage

You can use cURL to access this model:

```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "Asun Brysselissä, Euroopan pääkaupungissa."}' https://api-inference.huggingface.co/models/iguanodon-ai/bert-base-finnish-uncased-ner
```

Or Python API:

```
from transformers import AutoModelForTokenClassification, AutoTokenizer

model = AutoModelForTokenClassification.from_pretrained("iguanodon-ai/bert-base-finnish-uncased-ner")
tokenizer = AutoTokenizer.from_pretrained("iguanodon-ai/bert-base-finnish-uncased-ner")

inputs = tokenizer("Asun Brysselissä, Euroopan pääkaupungissa.", return_tensors="pt")
outputs = model(**inputs)
```
{"language": ["fi"], "license": "apache-2.0", "tags": ["token-classification"], "datasets": ["drvenabili/autotrain-data-turku-ner", "turku_ner_corpus"], "widget": [{"text": "Asun Brysseliss\u00e4, Euroopan p\u00e4\u00e4kaupungissa."}], "co2_eq_emissions": {"emissions": 0.2165403288824756}, "pipeline_tag": "token-classification"}
transformers pytorch safetensors bert token-classification fi dataset:drvenabili/autotrain-data-turku-ner dataset:turku_ner_corpus license:apache-2.0 co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #fi #dataset-drvenabili/autotrain-data-turku-ner #dataset-turku_ner_corpus #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# Info

This is a fine-tuned model on the NER task. The original model is Turku NLP's bert-base-finnish-uncased-v1, and the fine-tuning dataset is Turku NLP's turku_ner_corpus. 

The model is released under Apache 2.0.

Please mention the training dataset if you use this model:



# Validation Metrics

- Loss: 0.075
- Accuracy: 0.982
- Precision: 0.879
- Recall: 0.868
- F1: 0.873

# Test Metrics

### Overall Metrics

- Accuracy: 0.986
- Precision: 0.857
- Recall: 0.872
- F1: 0.864

### Per-entity metrics



## Usage

You can use cURL to access this model:



Or Python API:


# Info

This is a fine-tuned model on the NER task. The original model is Turku NLP's bert-base-finnish-uncased-v1, and the fine-tuning dataset is Turku NLP's turku_ner_corpus. 

The model is released under Apache 2.0.

Please mention the training dataset if you use this model: # Validation Metrics

- Loss: 0.075
- Accuracy: 0.982
- Precision: 0.879
- Recall: 0.868
- F1: 0.873 # Test Metrics ### Overall Metrics

- Accuracy: 0.986
- Precision: 0.857
- Recall: 0.872
- F1: 0.864 ### Per-entity metrics ## Usage

You can use cURL to access this model:



Or Python API:
TAGS
#transformers #pytorch #safetensors #bert #token-classification #fi #dataset-drvenabili/autotrain-data-turku-ner #dataset-turku_ner_corpus #license-apache-2.0 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # Info

This is a fine-tuned model on the NER task. The original model is Turku NLP's bert-base-finnish-uncased-v1, and the fine-tuning dataset is Turku NLP's turku_ner_corpus. 

The model is released under Apache 2.0.

Please mention the training dataset if you use this model: # Validation Metrics

- Loss: 0.075
- Accuracy: 0.982
- Precision: 0.879
- Recall: 0.868
- F1: 0.873 # Test Metrics ### Overall Metrics

- Accuracy: 0.986
- Precision: 0.857
- Recall: 0.872
- F1: 0.864 ### Per-entity metrics ## Usage

You can use cURL to access this model:



Or Python API: