---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- argument mining
datasets:
- US2016
- QT30
metrics:
- macro-f1
---

## ALBERT-based model for Argument Relation Identification (ARI)


Argument Mining model trained with English (EN) data for the Argument Relation Identification (ARI) task using the US2016 and the QT30 corpora. 
This a fine-tuned [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) model, inspired by "Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Domain Evaluation" paper.

<br>

This model was trained on the full dataset: train and test merged.

## Usage
```python

from transformers import BertTokenizer,BertForSequenceClassification

classes_decoder = {
        0: "Inference",
        1: "Conflict",
        2: "Rephrase",
        3: "No-Relation"
    }


model = BertForSequenceClassification.from_pretrained("yevhenkost/ArgumentMining-EN-ARI-AIF-ALBERT")
tokenizer = BertTokenizer.from_pretrained("yevhenkost/ArgumentMining-EN-ARI-AIF-ALBERT")

text_one, text_two = "The water is wet", "The sun is really hot"

model_inputs = tokenizer(text_one, text_two, return_tensors="pt")

# regular SequenceClassifierOutput
model_output = model(**model_inputs)
```

## Metrics

```
              precision    recall  f1-score   support

           0       0.51      0.59      0.55       833
           1       0.46      0.28      0.35       200
           2       0.51      0.30      0.38       156
           3       0.82      0.82      0.82      2209

    accuracy                           0.71      3398
   macro avg       0.58      0.50      0.53      3398
weighted avg       0.71      0.71      0.71      3398
```
Theses results for the model that was trained only on train chunk of data and tested on the test one.

Cite:

```
@article{ruiz2021transformer,
author = {R. Ruiz-Dolz and J. Alemany and S. Barbera and A. Garcia-Fornes},
journal = {IEEE Intelligent Systems},
title = {Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation},
year = {2021},
volume = {36},
number = {06},
issn = {1941-1294},
pages = {62-70},
doi = {10.1109/MIS.2021.3073993},
publisher = {IEEE Computer Society}
}

```