Instructions to use Godfrey2712/arg_mining_mm123_propositions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Godfrey2712/arg_mining_mm123_propositions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Godfrey2712/arg_mining_mm123_propositions")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Godfrey2712/arg_mining_mm123_propositions") model = AutoModelForSequenceClassification.from_pretrained("Godfrey2712/arg_mining_mm123_propositions") - Notebooks
- Google Colab
- Kaggle
Classifies Coherent Propositional Contents of locution Pairs as either RA (Support), CA (Attack), or N/A Argument Relationship. N/A consists of instances where there are other and no argument relationships present in the propositional content pairs.
Takes two coherent propositions and classifies as either Support, Attack, or N/A.
Results:
| precision | recall | f1-score | support | |
|---|---|---|---|---|
| CA | 0.38 | 0.27 | 0.32 | 22 |
| N/A | 0.79 | 0.85 | 0.82 | 284 |
| RA | 0.58 | 0.50 | 0.54 | 115 |
| accuracy | 0.72 | 421 | ||
| macro avg | 0.58 | 0.54 | 0.56 | 421 |
| weighted avg | 0.71 | 0.72 | 0.72 | 421 |
Dataset: https://corpora.aifdb.org/mm123 The data preprocessing and fine-tuning technique can be found here: https://discovery.dundee.ac.uk/en/studentTheses/exploiting-illocutionary-forces-in-dialogue-structures-for-enhanc/
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