README.md

---
datasets:
- artemkramov/coreference-dataset-ua
language:
- uk
tags:
- coreference-resolution
- anaphora
---
# 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 ([email protected]).
- **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)

### 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 |

#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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## Citation [optional]

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