Jos citat fali

README.md

@@ -6,6 +6,8 @@ datasets:
 - jnlpba
 - bigbio/n2c2_2018_track2
 - bigbio/bc5cdr
+widget:
+- text: Drug<SEP>He was given aspirin and paracetamol.
 language:
 - en
 metrics:
@@ -28,7 +30,7 @@ Model takes as input two strings. String1 is NER label. String1 must be phrase f
 model outputs list of zeros and ones corresponding to the occurance of Named Entity and corresponing to the tokens(tokens given by transformer tokenizer) of the Sring2.
 
 ## Example of usage
-```
+```python
 from transformers import AutoTokenizer
 from transformers import BertForTokenClassification
 
@@ -45,6 +47,49 @@ prediction_logits = model(**encodings)
 print(prediction_logits)
 ```
 
+## Available classes
+
+The following datasets and entities were used for training and therefore they can be used as label in the first segment (as a first string). Note that multiword string have been merged.
+
+
+* NCBI
+  * Specific Disease 
+  * Composite Mention 
+  * Modifier 
+  * Disease Class
+* BIORED
+  * Sequence Variant 
+  * Gene Or Gene Product 
+  * Disease Or Phenotypic Feature 
+  * Chemical Entity 
+  * Cell Line 
+  * Organism Taxon 
+* CDR Disease 
+  * Chemical
+* CHEMDNER
+  * Chemical
+  * Chemical Family
+* JNLPBA
+  * Protein
+  * DNA 
+  * Cell Type 
+  * Cell Line 
+  * RNA 
+* n2c2
+  * Drug
+  * Frequency 
+  * Strength
+  * Dosage
+  * Form
+  * Reason
+  * Route
+  * ADE 
+  * Duration
+
+On top of this, one can use the model in zero-shot regime with other classes, and also fine-tune it with few examples of other classes. 
+
+
+
 ## Code availibility
 
 Code used for training and testing the model is available at https://github.com/br-ai-ns-institute/Zero-ShotNER