Create README.md

README.md

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+---
+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.
+
+
+## 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)
+```
+
+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}
+}
+
+```