
# SpanMarker for Named Entity Recognition

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the underlying encoder. See [train.py](train.py) for the training script.
Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call `model.predict` with a 🤗 Dataset with `tokens`, `document_id` and `sentence_id` columns.
See the [documentation](https://tomaarsen.github.io/SpanMarkerNER/api/span_marker.modeling.html#span_marker.modeling.SpanMarkerModel.predict) of the `model.predict` method for more information.

## Usage

To use this model for inference, first install the `span_marker` library:

```bash
pip install span_marker
```

You can then run inference with this model like so:

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-conllpp-doc-context")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
```

### Limitations

**Warning**: This model works best when punctuation is separated from the prior words, so 
```python
# ✅
model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .")
# ❌
model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.")

# You can also supply a list of words directly: ✅
model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."])
```
The same may be beneficial for some languages, such as splitting `"l'ocean Atlantique"` into `"l' ocean Atlantique"`.

See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
{"language": ["en"], "license": "apache-2.0", "library_name": "span-marker", "tags": ["span-marker", "token-classification", "ner", "named-entity-recognition"], "datasets": ["conllpp", "tomaarsen/conllpp"], "metrics": ["f1", "recall", "precision"], "pipeline_tag": "token-classification", "widget": [{"text": "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris .", "example_title": "Amelia Earhart"}], "model-index": [{"name": "SpanMarker w. xlm-roberta-large on CoNLL++ with document-level context by Tom Aarsen", "results": [{"task": {"type": "token-classification", "name": "Named Entity Recognition"}, "dataset": {"name": "CoNLL++ w. document context", "type": "conllpp", "split": "test", "revision": "3e6012875a688903477cca9bf1ba644e65480bd6"}, "metrics": [{"type": "f1", "value": 0.9554, "name": "F1"}, {"type": "precision", "value": 0.96, "name": "Precision"}, {"type": "recall", "value": 0.9509, "name": "Recall"}]}]}]}
span-marker pytorch safetensors token-classification ner named-entity-recognition en dataset:conllpp dataset:tomaarsen/conllpp license:apache-2.0 model-index region:us
TAGS
#span-marker #pytorch #safetensors #token-classification #ner #named-entity-recognition #en #dataset-conllpp #dataset-tomaarsen/conllpp #license-apache-2.0 #model-index #region-us 


# SpanMarker for Named Entity Recognition

This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See URL for the training script.
Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call 'model.predict' with a  Dataset with 'tokens', 'document_id' and 'sentence_id' columns.
See the documentation of the 'model.predict' method for more information.

## Usage

To use this model for inference, first install the 'span_marker' library:



You can then run inference with this model like so:



### Limitations

Warning: This model works best when punctuation is separated from the prior words, so 

The same may be beneficial for some languages, such as splitting '"l'ocean Atlantique"' into '"l' ocean Atlantique"'.

See the SpanMarker repository for documentation and additional information on this library.
# SpanMarker for Named Entity Recognition

This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See URL for the training script.
Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call 'model.predict' with a  Dataset with 'tokens', 'document_id' and 'sentence_id' columns.
See the documentation of the 'model.predict' method for more information. ## Usage

To use this model for inference, first install the 'span_marker' library:



You can then run inference with this model like so: ### Limitations

Warning: This model works best when punctuation is separated from the prior words, so 

The same may be beneficial for some languages, such as splitting '"l'ocean Atlantique"' into '"l' ocean Atlantique"'.

See the SpanMarker repository for documentation and additional information on this library.
TAGS
#span-marker #pytorch #safetensors #token-classification #ner #named-entity-recognition #en #dataset-conllpp #dataset-tomaarsen/conllpp #license-apache-2.0 #model-index #region-us 
 # SpanMarker for Named Entity Recognition

This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses xlm-roberta-large as the underlying encoder. See URL for the training script.
Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call 'model.predict' with a  Dataset with 'tokens', 'document_id' and 'sentence_id' columns.
See the documentation of the 'model.predict' method for more information. ## Usage

To use this model for inference, first install the 'span_marker' library:



You can then run inference with this model like so: ### Limitations

Warning: This model works best when punctuation is separated from the prior words, so 

The same may be beneficial for some languages, such as splitting '"l'ocean Atlantique"' into '"l' ocean Atlantique"'.

See the SpanMarker repository for documentation and additional information on this library.