# Coreference resolution model for the Ukrainian language

<!-- Provide a quick summary of what the model is/does. -->

The coreference resolution model for the Ukrainian language was trained on the [silver Ukrainian coreference dataset](https://huggingface.co/datasets/artemkramov/coreference-dataset-ua)
using the [F-Coref](https://arxiv.org/abs/2209.04280) library. The model was trained on top of the [XML-Roberta-base model](https://huggingface.co/ukr-models/xlm-roberta-base-uk).


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Artem Kramov](https://www.linkedin.com/in/artem-kramov-0b3731100/), Andrii Kursin (aqrsn@ukr.net).
- **Languages:** Ukrainian
- **Finetuned from model:** [XML-Roberta-base](https://huggingface.co/ukr-models/xlm-roberta-base-uk) 

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/artemkramov/fastcoref-ua/blob/main/README.md
- **Demo:** [Google Colab](https://colab.research.google.com/drive/1vsaH15DFDrmKB4aNsQ-9TCQGTW73uk1y?usp=sharing), [Streamlit](https://coreference-ua-app.streamlit.app)

### Out-of-Scope Use

According to the metrics retrieved from the evaluation dataset, the model is more precision-oriented. Also, there is a high level of granularity of mentions. 
E.g., the mention "Головний виконавчий директор Андрій Сидоренко" can be divided into the following coreferent groups: ["Головний виконавчий директор Андрій Сидоренко", "Головний виконавчий директор", "Андрій Сидоренко"].
Such a feature can also be used to extract some positions, roles, or other features of entities in the text.


## How to Get Started with the Model

Use the code below to get started with the model.

```python
from fastcoref import FCoref
import spacy

nlp = spacy.load('uk_core_news_md')

model_path = "artemkramov/coref-ua"
model = FCoref(model_name_or_path=model_path, device='cuda:0', nlp=nlp)

preds = model.predict(
   texts=["""Мій друг дав мені свою машину та ключі до неї; крім того, він дав мені його книгу. Я з радістю її читаю."""]
)

preds[0].get_clusters(as_strings=False)
> [[(0, 3), (13, 17), (66, 70), (83, 84)],
 [(0, 8), (18, 22), (58, 61), (71, 75)],
 [(18, 29), (42, 45)],
 [(71, 81), (95, 97)]]

preds[0].get_clusters()
> [['Мій', 'мені', 'мені', 'Я'], ['Мій друг', 'свою', 'він', 'його'], ['свою машину', 'неї'], ['його книгу', 'її']]

preds[0].get_logit(
   span_i=(13, 17), span_j=(42, 45)
)

> -6.867196
```

## Training Details

### Training Data

The model was trained on the silver coreference resolution dataset: https://huggingface.co/datasets/artemkramov/coreference-dataset-ua.

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

#### Metrics

Two types of metrics were considered: mention-based and the coreference resolution metrics themselves.

Mention-based metrics:
- mention precision
- mention recall
- mention F1

Coreference resolution metrics were calculated as the average values across the following metrics: MUC, BCubed, CEAFE:
- coreference precision
- coreference recall
- coreference F1

### Results

The metrics for the validation dataset:

|         Metric        | Value |
|:---------------------|:-------|
| Mention precision     | 0.850 |
| Mention recall        | 0.798 |
| Mention F1            | 0.824 |
| Coreference precision | 0.758 |
| Coreference recall    | 0.706 |
| Coreference F1        | 0.731 |

## Model Card Authors

Artem Kramov (https://www.linkedin.com/in/artem-kramov-0b3731100/), Andrii Kursin (aqrsn@ukr.net)
{"language": ["uk"], "tags": ["coreference-resolution", "anaphora"], "datasets": ["artemkramov/coreference-dataset-ua"], "widget": [{"text": "Jens Peter Hansen kommer fra Danmark", "example_title": "Coreference resolution"}], "model-index": [{"name": "test", "results": [{"task": {"type": "coreference-resolution", "name": "Coreference resolution"}, "dataset": {"name": "Silver Ukrainian Coreference Resolution Dataset", "type": "artemkramov/coreference-dataset-ua"}, "metrics": [{"type": "coval", "value": 0.731, "name": "Mean F1 measure of MUC, BCubed, and CEAFE"}]}]}]}
transformers pytorch xlm-roberta coreference-resolution anaphora uk dataset:artemkramov/coreference-dataset-ua arxiv:2209.04280 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #xlm-roberta #coreference-resolution #anaphora #uk #dataset-artemkramov/coreference-dataset-ua #arxiv-2209.04280 #model-index #endpoints_compatible #has_space #region-us 

Coreference resolution model for the Ukrainian language
=======================================================


The coreference resolution model for the Ukrainian language was trained on the silver Ukrainian coreference dataset
using the F-Coref library. The model was trained on top of the XML-Roberta-base model.


Model Details
-------------


### Model Description


* Developed by: Artem Kramov, Andrii Kursin (aqrsn@URL).
* Languages: Ukrainian
* Finetuned from model: XML-Roberta-base


### Model Sources


* Repository: URL
* Demo: Google Colab, Streamlit


### Out-of-Scope Use


According to the metrics retrieved from the evaluation dataset, the model is more precision-oriented. Also, there is a high level of granularity of mentions.
E.g., the mention "Головний виконавчий директор Андрій Сидоренко" can be divided into the following coreferent groups: ["Головний виконавчий директор Андрій Сидоренко", "Головний виконавчий директор", "Андрій Сидоренко"].
Such a feature can also be used to extract some positions, roles, or other features of entities in the text.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
----------------


### Training Data


The model was trained on the silver coreference resolution dataset: URL


Evaluation
----------


#### Metrics


Two types of metrics were considered: mention-based and the coreference resolution metrics themselves.


Mention-based metrics:


* mention precision
* mention recall
* mention F1


Coreference resolution metrics were calculated as the average values across the following metrics: MUC, BCubed, CEAFE:


* coreference precision
* coreference recall
* coreference F1


### Results


The metrics for the validation dataset:



Model Card Authors
------------------


Artem Kramov (URL Andrii Kursin (aqrsn@URL)



### Model Description


* Developed by: Artem Kramov, Andrii Kursin (aqrsn@URL).
* Languages: Ukrainian
* Finetuned from model: XML-Roberta-base ### Model Sources


* Repository: URL
* Demo: Google Colab, Streamlit ### Out-of-Scope Use


According to the metrics retrieved from the evaluation dataset, the model is more precision-oriented. Also, there is a high level of granularity of mentions.
E.g., the mention "Головний виконавчий директор Андрій Сидоренко" can be divided into the following coreferent groups: ["Головний виконавчий директор Андрій Сидоренко", "Головний виконавчий директор", "Андрій Сидоренко"].
Such a feature can also be used to extract some positions, roles, or other features of entities in the text.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
---------------- ### Training Data


The model was trained on the silver coreference resolution dataset: URL


Evaluation
---------- #### Metrics


Two types of metrics were considered: mention-based and the coreference resolution metrics themselves.


Mention-based metrics:


* mention precision
* mention recall
* mention F1


Coreference resolution metrics were calculated as the average values across the following metrics: MUC, BCubed, CEAFE:


* coreference precision
* coreference recall
* coreference F1 ### Results


The metrics for the validation dataset:



Model Card Authors
------------------


Artem Kramov (URL Andrii Kursin (aqrsn@URL)
TAGS
#transformers #pytorch #xlm-roberta #coreference-resolution #anaphora #uk #dataset-artemkramov/coreference-dataset-ua #arxiv-2209.04280 #model-index #endpoints_compatible #has_space #region-us 
 ### Model Description


* Developed by: Artem Kramov, Andrii Kursin (aqrsn@URL).
* Languages: Ukrainian
* Finetuned from model: XML-Roberta-base ### Model Sources


* Repository: URL
* Demo: Google Colab, Streamlit ### Out-of-Scope Use


According to the metrics retrieved from the evaluation dataset, the model is more precision-oriented. Also, there is a high level of granularity of mentions.
E.g., the mention "Головний виконавчий директор Андрій Сидоренко" can be divided into the following coreferent groups: ["Головний виконавчий директор Андрій Сидоренко", "Головний виконавчий директор", "Андрій Сидоренко"].
Such a feature can also be used to extract some positions, roles, or other features of entities in the text.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
---------------- ### Training Data


The model was trained on the silver coreference resolution dataset: URL


Evaluation
---------- #### Metrics


Two types of metrics were considered: mention-based and the coreference resolution metrics themselves.


Mention-based metrics:


* mention precision
* mention recall
* mention F1


Coreference resolution metrics were calculated as the average values across the following metrics: MUC, BCubed, CEAFE:


* coreference precision
* coreference recall
* coreference F1 ### Results


The metrics for the validation dataset:



Model Card Authors
------------------


Artem Kramov (URL Andrii Kursin (aqrsn@URL)
TAGS
#transformers #pytorch #xlm-roberta #coreference-resolution #anaphora #uk #dataset-artemkramov/coreference-dataset-ua #arxiv-2209.04280 #model-index #endpoints_compatible #has_space #region-us 
### Model Description


* Developed by: Artem Kramov, Andrii Kursin (aqrsn@URL).
* Languages: Ukrainian
* Finetuned from model: XML-Roberta-base### Model Sources


* Repository: URL
* Demo: Google Colab, Streamlit### Out-of-Scope Use


According to the metrics retrieved from the evaluation dataset, the model is more precision-oriented. Also, there is a high level of granularity of mentions.
E.g., the mention "Головний виконавчий директор Андрій Сидоренко" can be divided into the following coreferent groups: ["Головний виконавчий директор Андрій Сидоренко", "Головний виконавчий директор", "Андрій Сидоренко"].
Such a feature can also be used to extract some positions, roles, or other features of entities in the text.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
----------------### Training Data


The model was trained on the silver coreference resolution dataset: URL


Evaluation
----------#### Metrics


Two types of metrics were considered: mention-based and the coreference resolution metrics themselves.


Mention-based metrics:


* mention precision
* mention recall
* mention F1


Coreference resolution metrics were calculated as the average values across the following metrics: MUC, BCubed, CEAFE:


* coreference precision
* coreference recall
* coreference F1### Results


The metrics for the validation dataset:



Model Card Authors
------------------


Artem Kramov (URL Andrii Kursin (aqrsn@URL)