Update README.md

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README.md

@@ -8,4 +8,95 @@ pipeline_tag: text-classification
 library_name: transformers
 tags:
 - news
----
+---
+
+
+### Description
+`polarity3c` is a classification model that is specialized for determining the polarity of texts from news portals. It was learned mostly on texts in Polish.
+
+<center><img src="https://cdn-uploads.huggingface.co/production/uploads/644addfe9279988e0cbc296b/v6pz2sBwc3GCPL1Il8wVP.png" width=20%></center>
+
+Annotations from the plWordnet were used as the basis for the data. A pre-learned model on these annotations, served as the model in Human in the loop,
+to support the annotations for teaching the final model.  The final model was learned on web content; data was manually collected and annotated.
+
+As a model base, the `sdadas/polish-roberta-large-v2` model was used with a classification head. More about model construction is on out [blog](https://radlab.dev/2025/06/01/polaryzacja-3c-model-z-plg-na-hf/).
+
+### Architecture
+```
+RobertaForSequenceClassification(
+  (roberta): RobertaModel(
+    (embeddings): RobertaEmbeddings(
+      (word_embeddings): Embedding(128001, 1024, padding_idx=1)
+      (position_embeddings): Embedding(514, 1024, padding_idx=1)
+      (token_type_embeddings): Embedding(1, 1024)
+      (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+      (dropout): Dropout(p=0.1, inplace=False)
+    )
+    (encoder): RobertaEncoder(
+      (layer): ModuleList(
+        (0-23): 24 x RobertaLayer(
+          (attention): RobertaAttention(
+            (self): RobertaSdpaSelfAttention(
+              (query): Linear(in_features=1024, out_features=1024, bias=True)
+              (key): Linear(in_features=1024, out_features=1024, bias=True)
+              (value): Linear(in_features=1024, out_features=1024, bias=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (output): RobertaSelfOutput(
+              (dense): Linear(in_features=1024, out_features=1024, bias=True)
+              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (intermediate): RobertaIntermediate(
+            (dense): Linear(in_features=1024, out_features=4096, bias=True)
+            (intermediate_act_fn): GELUActivation()
+          )
+          (output): RobertaOutput(
+            (dense): Linear(in_features=4096, out_features=1024, bias=True)
+            (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
+            (dropout): Dropout(p=0.1, inplace=False)
+          )
+        )
+      )
+    )
+  )
+  (classifier): RobertaClassificationHead(
+    (dense): Linear(in_features=1024, out_features=1024, bias=True)
+    (dropout): Dropout(p=0.1, inplace=False)
+    (out_proj): Linear(in_features=1024, out_features=3, bias=True)
+  )
+)
+```
+
+### Usage
+Example of use with transformers pipeline:
+```[python]
+from transformers import pipeline
+
+classifier = pipeline(model="radlab/polarity-3c", task="text-classification")
+
+classifier("Text to classification")
+```
+
+with sample data and `top_k=3`:
+```[python]
+classifier("""
+  Po upadku reżimu Asada w Syrii, mieszkańcy, borykający się z ubóstwem,
+  zaczęli tłumnie poszukiwać skarbów, zachęceni legendami o zakopanych
+  bogactwach i dostępnością wykrywaczy metali, które stały się popularnym
+  towarem. Mimo, że działalność ta jest nielegalna, rząd przymyka oko,
+  a sprzedawcy oferują urządzenia nawet dla dzieci. Poszukiwacze skupiają
+  się na obszarach historycznych, wierząc w legendy o skarbach ukrytych
+  przez starożytne cywilizacje i wojska osmańskie, choć eksperci ostrzegają
+  przed fałszywymi monetami i kradzieżą artefaktów z muzeów.""",
+  top_k=3
+)
+```
+the output is:
+```
+[{'label': 'ambivalent', 'score': 0.9995126724243164},
+ {'label': 'negative', 'score': 0.00024663121439516544},
+ {'label': 'positive', 'score': 0.00024063512682914734}]
+```
+