Create README.md

README.md

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+---
+datasets:
+- HiTZ/AbstRCT-ES
+language:
+- es
+- en
+pipeline_tag: token-classification
+---
+This model is a fine-tuned version of mBERT for the argument mining task using AbstRCT data in English and Spanish.  
+The dataset consists of abstracts of 5 disease types for argument component detection and argument relation classification:
+
+- `neoplasm`: 350 train, 100 dev and 50 test abstracts
+- `glaucoma_test`: 100 abstracts
+- `mixed_test`: 100 abstracts (20 on glaucoma, 20 on neoplasm, 20 on diabetes, 20 on hypertension, 20 on hepatitis) 
+
+The results achieved for each test set: 
+
+   Test   | F1-macro | F1-Claim | F1-Premise 
+--|-------|-------|-------
+ Neoplasm |   82.36  |  74.89   |   89.07    
+ Glaucoma |   80.52  |  75.22   |   84.86     
+   Mixed  |   81.69  |  75.06   |   88.57  
+
+```python
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained('HiTZ/mbert-argument-mining-es')
+```   
+
+