checkpoint-10

.gitattributes

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README.md

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+---
+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, List, Any
+from transformers import pipeline
+import holidays
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.pipeline = pipeline("text-classification", model=path)
+        self.holidays = holidays.US()
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+         data args:
+              inputs (:obj: `str`)
+              date (:obj: `str`)
+        Return:
+              A :obj:`list` | `dict`: will be serialized and returned
+        """
+        # get inputs
+        inputs = data.pop("inputs", data)
+        # get additional date field
+        date = data.pop("date", None)
+
+        # check if date exists and if it is a holiday
+        if date is not None and date in self.holidays:
+            return [{"label": "happy", "score": 1}]
+
+        # run normal prediction
+        prediction = self.pipeline(inputs)
+        return prediction

pytorch_model.bin

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+size 267875479

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"}