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distilbert-base-uncased-emotion/.gitattributes

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distilbert-base-uncased-emotion/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)

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
-}

distilbert-base-uncased-emotion/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)
-        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

distilbert-base-uncased-emotion/pytorch_model.bin

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-version https://git-lfs.github.com/spec/v1
-oid sha256:5aa7398d830fcc94f95af88d7cc3013813668cfc58a07d75a8116cfd8af75c4d
-size 267875479

distilbert-base-uncased-emotion/requirements.txt

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-holidays

distilbert-base-uncased-emotion/special_tokens_map.json

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

distilbert-base-uncased-emotion/test.py

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-from handler import EndpointHandler
-
-# init handler
-my_handler = EndpointHandler(path=".")
-
-# prepare sample payload
-non_holiday_payload = {"inputs": "I am quite excited how this will turn out", "date": "2022-08-08"}
-holiday_payload = {"inputs": "Nobody can do it", "date": "2022-07-04"}
-
-# test the handler
-non_holiday_pred=my_handler(non_holiday_payload)
-holiday_payload=my_handler(holiday_payload)
-
-# show results
-print("non_holiday_pred", non_holiday_pred)
-print("holiday_payload", holiday_payload)
-
-# non_holiday_pred [{'label': 'joy', 'score': 0.9985942244529724}]
-# holiday_payload [{'label': 'happy', 'score': 1}]

distilbert-base-uncased-emotion/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"}