Trying to enable model pipeline

README.md

@@ -1,9 +1,17 @@
 ---
 language:
-- uk
+  - uk
 tags:
-- token-classification
+  - token-classification
+  - punctuation prediction
+  - punctuation
 license: mit
 metrics:
-- f1
----
+  - f1
+---
+
+# Ukrainian model to restore punctuation and capitalization
+
+This is the NeMo model to restore punctuation and capitalization in sentences, trained on 10m+ sentences from UberText 2.0 corpus (yet unreleased)
+
+Model restores the following punctuations -- [? . ,]

pipeline.py

@@ -0,0 +1,44 @@
+from typing import Dict, List, Any
+from nemo.collections.nlp.models import PunctuationCapitalizationModel
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # Preload all the elements you are going to need at inference.
+        # For instance your model, processors, tokenizer that might be needed.
+        # This function is only called once, so do all the heavy processing I/O here"""
+        self.model = PunctuationCapitalizationModel.from_pretrained("dchaplinsky/punctuation_uk_bert")
+
+    def __call__(self, inputs: str) -> List[Dict[str, Any]]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string containing some text
+        Return:
+            A :obj:`list`:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
+                - "entity_group": A string representing what the entity is.
+                - "word": A substring of the original string that was detected as an entity.
+                - "start": the offset within `input` leading to `answer`. context[start:stop] == word
+                - "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
+                - "score": A score between 0 and 1 describing how confident the model is for this entity.
+        """
+        inputs = inputs.strip()
+        labels = self.model.add_punctuation_capitalization([inputs], return_labels=True)[0].split()
+
+        tokens = inputs.split()
+
+        res: List[Dict[str, Any]] = []
+        offset = 0
+        for tok, lab in zip(tokens, labels):
+            if lab != "OO":
+                res.append({
+                    "entity_group": lab,
+                    "word": tok,
+                    "start": offset,
+                    "end": offset + len(tok),
+                    "score": 1
+                })
+
+            offset += len(tok) + 1
+
+        return res

requirements.txt

@@ -0,0 +1 @@
+requirements.txt