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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - text-classification
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+ - emotion
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+ - endpoints-template
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+ license: apache-2.0
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+ datasets:
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+ - emotion
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+ metrics:
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+ - Accuracy, F1 Score
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+ ---
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+
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+
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+ # Fork of [bhadresh-savani/distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion)
__pycache__/handler.cpython-310.pyc ADDED
Binary file (1.17 kB). View file
 
config.json ADDED
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+ {
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+ "_name_or_path": "./",
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+ "activation": "gelu",
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+ "architectures": [
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+ "DistilBertForSequenceClassification"
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+ ],
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+ "attention_dropout": 0.1,
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+ "dim": 768,
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+ "dropout": 0.1,
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+ "hidden_dim": 3072,
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+ "id2label": {
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+ "0": "sadness",
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+ "1": "joy",
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+ "2": "love",
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+ "3": "anger",
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+ "4": "fear",
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+ "5": "surprise"
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+ },
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+ "initializer_range": 0.02,
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+ "label2id": {
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+ "anger": 3,
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+ "fear": 4,
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+ "joy": 1,
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+ "love": 2,
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+ "sadness": 0,
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+ "surprise": 5
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+ },
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+ "max_position_embeddings": 512,
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+ "model_type": "distilbert",
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+ "n_heads": 12,
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+ "n_layers": 6,
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+ "pad_token_id": 0,
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+ "qa_dropout": 0.1,
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+ "seq_classif_dropout": 0.2,
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+ "sinusoidal_pos_embds": false,
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+ "tie_weights_": true,
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+ "transformers_version": "4.11.0.dev0",
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+ "vocab_size": 30522
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+ }
handler.py ADDED
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+ from typing import Dict, List, Any
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+ from transformers import pipeline
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+ import holidays
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+
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+
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+ class EndpointHandler:
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+ def __init__(self, path=""):
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+ self.pipeline = pipeline("text-classification", model=path)
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+ self.holidays = holidays.US()
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+
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+ def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
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+ """
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+ data args:
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+ inputs (:obj: `str`)
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+ date (:obj: `str`)
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+ Return:
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+ A :obj:`list` | `dict`: will be serialized and returned
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+ """
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+ # get inputs
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+ inputs = data.pop("inputs", data)
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+ # get additional date field
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+ date = data.pop("date", None)
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+
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+ # check if date exists and if it is a holiday
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+ if date is not None and date in self.holidays:
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+ return [{"label": "happy", "score": 1}]
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+
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+ # run normal prediction
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+ prediction = self.pipeline(inputs)
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+ return prediction
pytorch_model.bin ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:5aa7398d830fcc94f95af88d7cc3013813668cfc58a07d75a8116cfd8af75c4d
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+ size 267875479
requirements.txt ADDED
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+ holidays
special_tokens_map.json ADDED
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+ {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
test.py ADDED
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+ from handler import EndpointHandler
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+
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+ # init handler
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+ my_handler = EndpointHandler(path=".")
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+
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+ # prepare sample payload
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+ non_holiday_payload = {"inputs": "I am quite excited how this will turn out", "date": "2022-08-08"}
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+ holiday_payload = {"inputs": "Today is a though day", "date": "2022-07-04"}
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+
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+ # test the handler
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+ non_holiday_pred=my_handler(non_holiday_payload)
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+ holiday_payload=my_handler(holiday_payload)
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+
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+ # show results
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+ print("non_holiday_pred", non_holiday_pred)
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+ print("holiday_payload", holiday_payload)
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+
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+ # non_holiday_pred [{'label': 'joy', 'score': 0.9985942244529724}]
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+ # holiday_payload [{'label': 'happy', 'score': 1}]
tokenizer_config.json ADDED
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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"}
vocab.txt ADDED
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