File size: 2,218 Bytes
4573d91 4b25283 4573d91 4b25283 4573d91 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
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}
}
``` |