Upload folder using huggingface_hub

config.json

@@ -0,0 +1,4 @@
+{
+  "model_type": "custom-onnx",
+  "pipeline_tag": "text-classification"
+}

pipeline_imdb_cnn.py

@@ -0,0 +1,41 @@
+
+import numpy as np
+import onnxruntime as ort
+from transformers import Pipeline
+from tensorflow.keras.datasets import imdb
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+
+class ImdbCnnPipeline(Pipeline):
+    def __init__(self, model, tokenizer=None, **kwargs):
+        super().__init__(model=model, tokenizer=tokenizer, **kwargs)
+
+        # Загружаем словарь
+        word_index = imdb.get_word_index()
+        word_index = {k: (v + 3) for k, v in word_index.items()}
+        word_index["<PAD>"] = 0
+        word_index["<START>"] = 1
+        word_index["<UNK>"] = 2
+        word_index["<UNUSED>"] = 3
+        self.word_index = word_index
+
+    def _sanitize_parameters(self, **kwargs):
+        return {}, {}, {}
+
+    def preprocess(self, text):
+        tokens = text.lower().split()
+        encoded = [self.word_index.get(word, 2) for word in tokens]
+        padded = pad_sequences([encoded], maxlen=500, value=0, padding='post')
+        return {"input": padded.astype(np.int32)}
+
+    def _forward(self, model_inputs):
+        input_ids = model_inputs["input"]
+        ort_inputs = {self.model.get_inputs()[0].name: input_ids}
+        logits = self.model.run(None, ort_inputs)[0]
+        return {"logits": logits}
+
+    def postprocess(self, model_outputs):
+        pred = model_outputs["logits"][0][0]
+        label = "POSITIVE" if pred > 0.5 else "NEGATIVE"
+        confidence = float(pred) if pred > 0.5 else 1 - float(pred)
+        return {"label": label, "confidence": confidence}

tokenizer_config.json

@@ -0,0 +1,3 @@
+{
+  "tokenizer_class": "CustomTokenizer"
+}